One of the goals of the RDoC project is to relate measures of working memory across modalities. In the fMRI data, we found differences in load effects based on working memory capacity - typically an inverted U shape relationship between capacity and load effects. Here, we look to investigate whether there are any siimilar relationships in the EEG data, in addition to attempting to replicate some findings in the literature.
ERP data:
During both the cue and probe, we see significant load effects in the N170 components in the average of the O1 and O2 electrodes and in the average of PO7 and PO8 (which is slightly stronger). For this effect, there’s a numerical inverted U shape relationship with capacity. We also see a load effect in the P3 in the parietal electrode average; for this effect, we see a linear increase with capacity. Both of these show a larger amplitude in the low load than high load.
When we look at differences across WM capacity groups, we see a linear increase in activity in P3 in the high load condition.
Spectral data:
Mid occipital cluster shows a positive correlation with span during the encoding and delay period in the beta and low gamma bands, and a negative correlation in the theta through alpha bands during the entire task, but most prominently right at the beginning of the probe period.
Oz shows a strong positive correlation with alpha power during encoding and what looks to be low gamma across the entire task. There also seems to be a negative correlation with beta across the entire task, with particularly strong negative correlations right at the end of the delay period/beginning of probe period.
F avg shows correlations during encoding with the theta band and low gamma/high beta that drift into the beginning of delay, and then a strong negative correlation right at the end of the delay period/beginning of the probe period.
There is also a strong correlation with the fMRI load effect ini the alpha/beta range across all electrodes, especially in encoding.
When we look at all subjects, we see load effects during the cue period in: - alpha, beta: Oz, mid occip, F avg (low > high) - theta: Oz electrode (high > low)
delay period: - alpha, theta: Oz electrode (high > low) - beta: Oz and mid occipital (high > low) - low gamma: mid occipital, Oz (low > high)
probe period; - alpha, beta, theta, low gamma: Oz, mid occip, F avg (low > high)
When we split spectral data into WMC groups, we see:
cue period: - theta: Oz electrode show larger electrodes in high WMC subjects - low gamma: mid occipital (high load and LE) and Oz show med WMC > high WMC (high load and LE) and mid occipital also shows low > high in raw power; trend towards inverted U shaped relationship for LEs in Oz electrode
delay period: - alpha: (non-significant) inverted U shape relationship in Oz - theta: mid occipital cluster shows negative relationship between capacity and LE and high load; more negative power in raw high load data in high WMC > low WMC - low gamma: mid occipital, Oz show low WMC > high WMC and Oz shows med WMC > high WMC in raw power during high load trials mid occipital, Oz electrode show low WMC > high WMC load effects
probe period: - alpha: mid occipital cluster high load and load effect - high WMC more neg raw power than low WMC and more neg LE than med WMC
For the high load, we see a significant relationship between WMC group and theta-beta coupling in the O2 electrode, with a positive linear trend. We also see trends in the Fz electrode for the theta-beta (inverted U shape relationship) and theta-hgamma PAC (negative linear trend). For the load effects, the only significant PAC measure across WMC groups is the theta - beta coupling in the Fz electrode, with an inverted U shaped relationship.
CDA only showed a relationship with the LCD K max measure in the 1 dot condition, but no correlation or obvious inverted U with the DFR load effects or our omnibus span measure. Numerically, it looks like there is an inverted U shaped relationship for the 5 dots - 1 dot and 3 dot - 1 dot, but those relationships are not significant.
library(tidyverse)
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## ✓ tibble 2.1.3 ✓ dplyr 0.8.3
## ✓ tidyr 1.0.0 ✓ stringr 1.4.0
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## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(patchwork)
library(rmatio)
library(reshape2)
##
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
##
## smiths
load("data/behav.RData")
load("data/load_effects_DFR.RData")
load("data/split_groups_info.RData")
source("helper_fxns/load_EEG_data.R")
source("helper_fxns/corr_spectrum_to_indiv_diff.R")
source("helper_fxns/mutate_for_heatmap.R")
source("helper_fxns/split_ERPs_into_groups.R")
source("helper_fxns/create_TC_for_plot.R")
source("helper_fxns/split_spectrum.R")
source("helper_fxns/paired_freq_plot.R")
source("helper_fxns/select_period_average.R")
source("helper_fxns/split_into_groups.R")
source("helper_fxns/prep_split_for_bar_plots.R")
source("helper_fxns/average_electrodes.R")
source("helper_fxns/average_electrodes_spectrum.R")
temp <- read.mat("data/EEG/DFR/ERPS_cl15_midOccip_reformatted.mat")
ERPS_times_DFR <- temp[["all_times"]]
temp <- read.mat("data/EEG/DFR/ERSPS_cl15_midOccip_reformatted.mat")
ERSPS_times_DFR <- temp[["all_times"]]
ERSPS_freqs <- temp[["all_freqs"]]
ERPS_midOccip_DFR <- load_EEG_data("DFR", "ERPS_cl15_midOccip")
ERSPS_midOccip_DFR <- load_EEG_data("DFR", "ERSPS_cl15_midOccip")
ERSPS_Oz_DFR <- load_EEG_data("DFR", "ERSPS_Oz")
ERPS_Pz_DFR <- load_EEG_data("DFR", "ERPS_Pz")
ERPS_P1_DFR <- load_EEG_data("DFR", "ERPS_P1")
ERPS_P2_DFR <- load_EEG_data("DFR", "ERPS_P2")
ERPS_CP1_DFR <- load_EEG_data("DFR", "ERPS_CP1")
ERPS_CP2_DFR <- load_EEG_data("DFR", "ERPS_CP2")
ERPS_POz_DFR <- load_EEG_data("DFR", "ERPS_POz")
ERSPS_Fz_DFR <- load_EEG_data("DFR", "ERSPS_Fz")
ERSPS_F1_DFR <- load_EEG_data("DFR", "ERSPS_F1")
ERSPS_F2_DFR <- load_EEG_data("DFR", "ERSPS_F2")
ERSPS_FC1_DFR <- load_EEG_data("DFR", "ERSPS_FC1")
ERSPS_FC2_DFR <- load_EEG_data("DFR", "ERSPS_FC2")
ERSPS_AFz_DFR <- load_EEG_data("DFR", "ERSPS_AFz")
ERPS_O1_DFR <- load_EEG_data("DFR", "ERPS_O1")
ERPS_O2_DFR <- load_EEG_data("DFR", "ERPS_O2")
ERPS_PO7_DFR <- load_EEG_data("DFR", "ERPS_PO7")
ERPS_PO8_DFR <- load_EEG_data("DFR", "ERPS_PO8")
ERPS_Oavg_DFR <- average_electrodes(list(ERPS_O1_DFR, ERPS_O2_DFR))
ERPS_POavg_DFR <- average_electrodes(list(ERPS_PO7_DFR, ERPS_PO8_DFR))
ERPS_Pavg_DFR <- average_electrodes(list(ERPS_Pz_DFR,ERPS_P1_DFR, ERPS_P2_DFR,ERPS_POz_DFR,ERPS_CP1_DFR, ERPS_CP2_DFR))
ERSPS_Favg_DFR <- average_electrodes_spectrum(list(ERSPS_Fz_DFR, ERSPS_F1_DFR, ERSPS_F2_DFR, ERSPS_AFz_DFR, ERSPS_FC1_DFR, ERSPS_FC2_DFR))
#save(list=c("CDA","CDA_fMRI","ERPS_times_DFR", "ERPS_times_LCD", "ERSPS_times_DFR", "ERSPS_times_LCD", "ERSPS_freqs", "ERPS_midOccip_LCD", "ERSPS_midOccip_LCD", "ERSPS_Oz_LCD","ERSPS_O2_LCD","ERSPS_O1_LCD", "ERPS_Pz_LCD","ERPS_Fz_LCD", "ERPS_midOccip_DFR", "ERSPS_midOccip_DFR", "ERSPS_Oz_DFR","ERPS_Pz_DFR"), file="data/newEEG_data.RData")
rects <- data.frame(xstart=c(0,5500),xend=c(2500,7000),col = "gray")
avg_ERPs_for_plot <- list(
midOccip_DFR = data.frame(high_load = apply(ERPS_midOccip_DFR[["high_load"]][,2:1922], 2, mean),
low_load = apply(ERPS_midOccip_DFR[["low_load"]][,2:1922], 2, mean),
load_effect = apply(ERPS_midOccip_DFR[["load_effect"]][,2:1922], 2, mean),
time =ERPS_times_DFR),
Pavg_DFR = data.frame(high_load = apply(ERPS_Pavg_DFR[["high_load"]][,2:1922], 2, mean),
low_load = apply(ERPS_Pavg_DFR[["low_load"]][,2:1922], 2, mean),
load_effect = apply(ERPS_Pavg_DFR[["load_effect"]][,2:1922], 2, mean),
time =ERPS_times_DFR),
Oavg_DFR = data.frame(high_load = apply(ERPS_Oavg_DFR[["high_load"]][,2:1922], 2, mean),
low_load = apply(ERPS_Oavg_DFR[["low_load"]][,2:1922], 2, mean),
load_effect = apply(ERPS_Oavg_DFR[["load_effect"]][,2:1922], 2, mean),
time =ERPS_times_DFR),
POavg_DFR = data.frame(high_load = apply(ERPS_POavg_DFR[["high_load"]][,2:1922], 2, mean),
low_load = apply(ERPS_POavg_DFR[["low_load"]][,2:1922], 2, mean),
load_effect = apply(ERPS_POavg_DFR[["load_effect"]][,2:1922], 2, mean),
time =ERPS_times_DFR)
)
plot_list <- list()
for (cluster in seq.int(1,length(avg_ERPs_for_plot))){
plot_list[[names(avg_ERPs_for_plot)[cluster]]][["indiv_loads"]] <- ggplot(data = avg_ERPs_for_plot[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=high_load))+
geom_line(aes(x=time,y=low_load),linetype="dotted")+
ylab("ERP")+
ggtitle("Low Load vs High Load")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
plot_list[[names(avg_ERPs_for_plot)[cluster]]][["LE"]] <- ggplot(data = avg_ERPs_for_plot[[cluster]])+
geom_line(aes(x=time,y=load_effect))+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
ylab("ERP")+
ggtitle("Load Effect")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
}
avg_P <- (avg_ERPs_for_plot[["Pavg_DFR"]]$high_load + avg_ERPs_for_plot[["Pavg_DFR"]]$low_load)/2
avg_O <- (avg_ERPs_for_plot[["Oavg_DFR"]]$high_load + avg_ERPs_for_plot[["Oavg_DFR"]]$low_load)/2
avg_PO <- (avg_ERPs_for_plot[["POavg_DFR"]]$high_load + avg_ERPs_for_plot[["POavg_DFR"]]$low_load)/2
avg_plots <- data.frame(time = avg_ERPs_for_plot[["Pavg_DFR"]]$time, avg_P, avg_O, avg_PO)
ggplot(data = avg_plots)+
geom_line(aes(x=time,y=avg_P))+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
ylab("ERP")+
ggtitle("Average P electrodes ERP")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
ggplot(data = avg_plots)+
geom_line(aes(x=time,y=avg_O))+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
ylab("ERP")+
ggtitle("Average O electrodes ERP")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
ggplot(data = avg_plots)+
geom_line(aes(x=time,y=avg_PO))+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
ylab("ERP")+
ggtitle("Average PO electrodes ERP")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
cue_P3_range <- c(which(avg_plots$avg_P == max(avg_plots$avg_P[229:290]))-6 , which(avg_plots$avg_P == max(avg_plots$avg_P[229:290]))+6)
probe_P3_range <- c(which(avg_plots$avg_P == max(avg_plots$avg_P[1664:1680]))-6 , which(avg_plots$avg_P == max(avg_plots$avg_P[1664:1680]))+6)
cue_n170_range <- c(which(avg_plots$avg_O == min(avg_plots$avg_O[129:198]))-6, which(avg_plots$avg_O == min(avg_plots$avg_O[129:198]))+6)
probe_n170_range <- c(which(avg_plots$avg_O == min(avg_plots$avg_O[1550:1677]))-6,which(avg_plots$avg_O == min(avg_plots$avg_O[1550:1677]))+6)
During both the cue and probe, we see significant load effects in the N170 components in the average of the O1 and O2 electrodes and the PO7 and PO8 averages. We also see a load effect in the P3 in the parietal electrode average. Both of these show a larger amplitude in the low load than high load.
plot_list[["midOccip_DFR"]][["indiv_loads"]] + plot_list[["midOccip_DFR"]][["LE"]] +
plot_annotation(title="midOccip - DFR")
plot_list[["Pavg_DFR"]][["indiv_loads"]] + plot_list[["Pavg_DFR"]][["LE"]] +
plot_annotation(title="P avg - DFR")
plot_list[["Oavg_DFR"]][["indiv_loads"]] + plot_list[["Oavg_DFR"]][["LE"]] +
plot_annotation(title="O avg - DFR")
plot_list[["POavg_DFR"]][["indiv_loads"]] + plot_list[["POavg_DFR"]][["LE"]] +
plot_annotation(title="PO avg - DFR")
cue_average_midOccip_n170 <- select_period_average(ERPS_midOccip_DFR,avg_plots$time[cue_n170_range[1]],avg_plots$time[cue_n170_range[2]],ERPS_times_DFR)
cue_average_O_n170 <- select_period_average(ERPS_Oavg_DFR,avg_plots$time[cue_n170_range[1]],avg_plots$time[cue_n170_range[2]],ERPS_times_DFR)
cue_average_PO_n170 <- select_period_average(ERPS_POavg_DFR,avg_plots$time[cue_n170_range[1]],avg_plots$time[cue_n170_range[2]],ERPS_times_DFR)
cue_average_P3 <- select_period_average(ERPS_Pavg_DFR,avg_plots$time[cue_P3_range[1]],avg_plots$time[cue_P3_range[2]],ERPS_times_DFR)
probe_average_midOccip_n170 <- select_period_average(ERPS_midOccip_DFR,avg_plots$time[probe_n170_range[1]],avg_plots$time[probe_n170_range[2]],ERPS_times_DFR)
probe_average_O_n170 <- select_period_average(ERPS_Oavg_DFR,avg_plots$time[probe_n170_range[1]],avg_plots$time[probe_n170_range[2]],ERPS_times_DFR)
probe_average_PO_n170 <- select_period_average(ERPS_POavg_DFR,avg_plots$time[probe_n170_range[1]],avg_plots$time[probe_n170_range[2]],ERPS_times_DFR)
probe_average_P3 <- select_period_average(ERPS_Pavg_DFR,avg_plots$time[probe_P3_range[1]],avg_plots$time[probe_P3_range[2]],ERPS_times_DFR)
t.test(cue_average_midOccip_n170$load_effect,mu=0)
##
## One Sample t-test
##
## data: cue_average_midOccip_n170$load_effect
## t = -1.0273, df = 177, p-value = 0.3057
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.08180836 0.02579485
## sample estimates:
## mean of x
## -0.02800676
t.test(cue_average_O_n170$load_effect,mu=0)
##
## One Sample t-test
##
## data: cue_average_O_n170$load_effect
## t = -5.2605, df = 189, p-value = 3.871e-07
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8283886 -0.3765553
## sample estimates:
## mean of x
## -0.602472
t.test(cue_average_PO_n170$load_effect,mu=0)
##
## One Sample t-test
##
## data: cue_average_PO_n170$load_effect
## t = -6.9534, df = 189, p-value = 5.642e-11
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.0704404 -0.5973194
## sample estimates:
## mean of x
## -0.8338799
t.test(cue_average_P3$load_effect,mu=0)
##
## One Sample t-test
##
## data: cue_average_P3$load_effect
## t = -5.1679, df = 189, p-value = 5.988e-07
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.6709431 -0.3002420
## sample estimates:
## mean of x
## -0.4855926
t.test(probe_average_midOccip_n170$load_effect,mu=0)
##
## One Sample t-test
##
## data: probe_average_midOccip_n170$load_effect
## t = 1.7801, df = 177, p-value = 0.07678
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.02215789 0.43009510
## sample estimates:
## mean of x
## 0.2039686
t.test(probe_average_O_n170$load_effect,mu=0)
##
## One Sample t-test
##
## data: probe_average_O_n170$load_effect
## t = 0.24254, df = 189, p-value = 0.8086
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.3504347 0.4486914
## sample estimates:
## mean of x
## 0.04912837
t.test(probe_average_PO_n170$load_effect,mu=0)
##
## One Sample t-test
##
## data: probe_average_PO_n170$load_effect
## t = -0.28819, df = 189, p-value = 0.7735
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8147653 0.6070440
## sample estimates:
## mean of x
## -0.1038607
t.test(probe_average_P3$load_effect,mu=0)
##
## One Sample t-test
##
## data: probe_average_P3$load_effect
## t = -4.9474, df = 189, p-value = 1.657e-06
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.7978863 -0.3430026
## sample estimates:
## mean of x
## -0.5704444
split_ERPS_midOccip_DFR <- split_ERPs_into_groups(ERPS_midOccip_DFR, WM_groups, ERPS_times_DFR)
split_ERPS_Pavg_DFR <- split_ERPs_into_groups(ERPS_Pavg_DFR, WM_groups, ERPS_times_DFR)
split_ERPS_Oavg_DFR <- split_ERPs_into_groups(ERPS_Oavg_DFR, WM_groups, ERPS_times_DFR)
split_ERPS_POavg_DFR <- split_ERPs_into_groups(ERPS_POavg_DFR, WM_groups, ERPS_times_DFR)
ERPs_for_plot <- list(mid_Occip_DFR = split_ERPS_midOccip_DFR,
Pavg_DFR = split_ERPS_Pavg_DFR,
Oavg_DFR = split_ERPS_Oavg_DFR,
POavg_DFR = split_ERPS_POavg_DFR)
split_ERPs_plot <- create_TC_for_plot(ERPs_for_plot)
ggplot(data = split_ERPs_plot[["mid_Occip_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["mid_Occip_DFR"]][["long"]] %>% filter(load=="high"),aes(x=Time,y=Mean,color=level)) +
geom_line(data=split_ERPs_plot[["mid_Occip_DFR"]][["long"]] %>% filter(load=="low"),aes(x=Time,y=Mean,color=level),linetype="dotted")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP")+
ggtitle("mid_Occip_DFR")+
theme_classic()
ggplot(data=split_ERPs_plot[["mid_Occip_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["mid_Occip_DFR"]][["long"]] %>% filter(load=="load_effect"),aes(x=Time,y=Mean,color=level)) +
ylab("Mean Activity") +
xlab("Time (ms)")+
geom_ribbon(data=split_ERPs_plot[["mid_Occip_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="high"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="red")+
geom_ribbon(data=split_ERPs_plot[["mid_Occip_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="med"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="green")+
geom_ribbon(data=split_ERPs_plot[["mid_Occip_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="low"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="blue")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP Load Effect")+
ggtitle("mid_Occip_DFR")+
theme_classic()
ggplot(data = split_ERPs_plot[["Pavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load=="high"),aes(x=Time,y=Mean,color=level)) +
geom_line(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load=="low"),aes(x=Time,y=Mean,color=level),linetype="dotted")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP")+
ggtitle("P avg DFR")+
theme_classic()
ggplot(data=split_ERPs_plot[["Pavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load=="load_effect"),aes(x=Time,y=Mean,color=level)) +
ylab("Mean Activity") +
xlab("Time (ms)")+
geom_ribbon(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="high"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="red")+
geom_ribbon(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="med"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="green")+
geom_ribbon(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="low"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="blue")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP Load Effect")+
ggtitle("P avg DFR")+
theme_classic()
ggplot(data = split_ERPs_plot[["Oavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load=="high"),aes(x=Time,y=Mean,color=level)) +
geom_line(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load=="low"),aes(x=Time,y=Mean,color=level),linetype="dotted")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP")+
ggtitle("O avg DFR")+
theme_classic()
ggplot(data=split_ERPs_plot[["Oavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load=="load_effect"),aes(x=Time,y=Mean,color=level)) +
ylab("Mean Activity") +
xlab("Time (ms)")+
geom_ribbon(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="high"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="red")+
geom_ribbon(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="med"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="green")+
geom_ribbon(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="low"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="blue")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP Load Effect")+
ggtitle("O avg DFR")+
theme_classic()
ggplot(data = split_ERPs_plot[["POavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load=="high"),aes(x=Time,y=Mean,color=level)) +
geom_line(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load=="low"),aes(x=Time,y=Mean,color=level),linetype="dotted")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP")+
ggtitle("PO avg DFR")+
theme_classic()
ggplot(data=split_ERPs_plot[["POavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load=="load_effect"),aes(x=Time,y=Mean,color=level)) +
ylab("Mean Activity") +
xlab("Time (ms)")+
geom_ribbon(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="high"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="red")+
geom_ribbon(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="med"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="green")+
geom_ribbon(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="low"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="blue")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP Load Effect")+
ggtitle("PO avg DFR")+
theme_classic()
cue_P3_DFR_split <- split_into_groups(cue_average_P3, WM_groups)
cue_midOccip_n170_DFR_split <- split_into_groups(cue_average_midOccip_n170, WM_groups)
cue_Oavg_n170_DFR_split <- split_into_groups(cue_average_O_n170, WM_groups)
cue_POavg_n170_DFR_split <- split_into_groups(cue_average_PO_n170, WM_groups)
probe_P3_DFR_split <- split_into_groups(probe_average_P3, WM_groups)
probe_midOccip_n170_DFR_split <- split_into_groups(probe_average_midOccip_n170, WM_groups)
probe_Oavg_n170_DFR_split <- split_into_groups(probe_average_O_n170, WM_groups)
probe_POavg_n170_DFR_split <- split_into_groups(probe_average_PO_n170, WM_groups)
cue_P3_DFR_split_prepped <- prep_split_for_bar_plots(cue_P3_DFR_split)
cue_midOccip_n170_DFR_split_prepped <- prep_split_for_bar_plots(cue_midOccip_n170_DFR_split)
cue_Oavg_n170_DFR_split_prepped <- prep_split_for_bar_plots(cue_Oavg_n170_DFR_split)
cue_POavg_n170_DFR_split_prepped <- prep_split_for_bar_plots(cue_POavg_n170_DFR_split)
probe_P3_DFR_split_prepped <- prep_split_for_bar_plots(probe_P3_DFR_split)
probe_midOccip_n170_DFR_split_prepped <- prep_split_for_bar_plots(probe_midOccip_n170_DFR_split)
probe_Oavg_n170_DFR_split_prepped <- prep_split_for_bar_plots(probe_Oavg_n170_DFR_split)
probe_POavg_n170_DFR_split_prepped <- prep_split_for_bar_plots(probe_POavg_n170_DFR_split)
WM_to_merge <- WM_groups[["all"]][,c(1,12)]
cue_midOccip_n170_DFR_anova <- merge(cue_average_midOccip_n170,WM_to_merge, by="PTID")
probe_midOccip_n170_DFR_anova <- merge(probe_average_midOccip_n170,WM_to_merge, by="PTID")
cue_Oavg_n170_DFR_anova <- merge(cue_average_O_n170,WM_to_merge, by="PTID")
probe_Oavg_n170_DFR_anova <- merge(probe_average_O_n170,WM_to_merge, by="PTID")
cue_POavg_n170_DFR_anova <- merge(cue_average_PO_n170,WM_to_merge, by="PTID")
probe_POavg_n170_DFR_anova <- merge(probe_average_PO_n170,WM_to_merge, by="PTID")
cue_P3_DFR_anova <- merge(cue_average_P3,WM_to_merge, by="PTID")
probe_P3_DFR_anova <- merge(probe_average_P3,WM_to_merge, by="PTID")
We see a linear increase in the P3 component at high load with capacity - there’s a larger amplitude with higher capacity.
We also do see the inverted U shaped relationship with the N170 component during cue for the occipital electrodes and component, but there’s high variance so it is not significant.
cue_P3_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_P3_high
cue_midOccip_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_midOccip_n170_high
cue_Oavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_Oavg_n170_high
cue_POavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_POavg_n170_high
probe_P3_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_P3_high
probe_midOccip_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_midOccip_n170_high
probe_Oavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_Oavg_n170_high
probe_POavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_POavg_n170_high
cue_P3_high + probe_P3_high+
plot_annotation(title="P3 during high load during DFR")
cue_midOccip_n170_high + probe_midOccip_n170_high+
plot_annotation(title="midOccip cluster n170 high load during DFR")
cue_Oavg_n170_high + probe_Oavg_n170_high+
plot_annotation(title="Oavg n170 high load during DFR")
cue_POavg_n170_high + probe_POavg_n170_high+
plot_annotation(title="POavg n170 high load during DFR")
cue_midOccip_n170_high.aov <- aov(high_load ~ level, data = cue_midOccip_n170_DFR_anova)
print(summary(cue_midOccip_n170_high.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.88 0.4385 0.598 0.551
## Residuals 148 108.48 0.7330
probe_midOccip_n170_high.aov <- aov(high_load ~ level, data = probe_midOccip_n170_DFR_anova)
print(summary(probe_midOccip_n170_high.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.86 0.4279 0.348 0.707
## Residuals 148 182.04 1.2300
cue_Oavg_n170_high.aov <- aov(high_load ~ level, data = cue_Oavg_n170_DFR_anova)
print(summary(cue_Oavg_n170_high.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 25.7 12.83 0.961 0.385
## Residuals 158 2110.5 13.36
probe_Oavg_n170_high.aov <- aov(high_load ~ level, data = probe_Oavg_n170_DFR_anova)
print(summary(probe_Oavg_n170_high.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 74.9 37.47 2.745 0.0673 .
## Residuals 158 2157.0 13.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
cue_POavg_n170_high.aov <- aov(high_load ~ level, data = cue_POavg_n170_DFR_anova)
print(summary(cue_POavg_n170_high.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 25.2 12.60 1.024 0.362
## Residuals 158 1944.7 12.31
probe_POavg_n170_high.aov <- aov(high_load ~ level, data = probe_POavg_n170_DFR_anova)
print(summary(probe_POavg_n170_high.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 44.3 22.17 1.609 0.203
## Residuals 158 2177.1 13.78
cue_P3_high.aov <- aov(high_load ~ level, data = cue_P3_DFR_anova)
print(summary(cue_P3_high.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 73.2 36.58 9.394 0.00014 ***
## Residuals 158 615.2 3.89
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
probe_P3_high.aov <- aov(high_load ~ level, data = probe_P3_DFR_anova)
print(summary(probe_P3_high.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 44.4 22.182 4.461 0.013 *
## Residuals 158 785.7 4.973
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
However, there is no difference in load effect
cue_P3_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_P3_LE
cue_midOccip_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_midOccip_n170_LE
cue_Oavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_Oavg_n170_LE
cue_POavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_POavg_n170_LE
probe_P3_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_P3_LE
probe_midOccip_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_midOccip_n170_LE
probe_Oavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_Oavg_n170_LE
probe_POavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_POavg_n170_LE
cue_P3_LE + probe_P3_LE+
plot_annotation(title="P3 Load Effect during DFR")
cue_midOccip_n170_LE + probe_midOccip_n170_LE+
plot_annotation(title="midOccip cluster n170 Load Effect during DFR")
cue_Oavg_n170_LE + probe_Oavg_n170_LE+
plot_annotation(title="Oavg n170 Load Effect during DFR")
cue_POavg_n170_LE + probe_POavg_n170_LE+
plot_annotation(title="POavg n170 Load Effect during DFR")
cue_midOccip_n170_LE.aov <- aov(load_effect ~ level, data = cue_midOccip_n170_DFR_anova)
print(summary(cue_midOccip_n170_LE.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.112 0.0560 0.439 0.646
## Residuals 148 18.882 0.1276
probe_midOccip_n170_LE.aov <- aov(load_effect ~ level, data = probe_midOccip_n170_DFR_anova)
print(summary(probe_midOccip_n170_LE.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.8 1.416 0.515 0.599
## Residuals 148 407.3 2.752
cue_Oavg_n170_LE.aov <- aov(load_effect ~ level, data = cue_Oavg_n170_DFR_anova)
print(summary(cue_Oavg_n170_LE.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.7 1.373 0.567 0.568
## Residuals 158 382.3 2.420
probe_Oavg_n170_LE.aov <- aov(load_effect ~ level, data = probe_Oavg_n170_DFR_anova)
print(summary(probe_Oavg_n170_LE.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.2 1.584 0.177 0.838
## Residuals 158 1412.4 8.939
cue_POavg_n170_LE.aov <- aov(load_effect ~ level, data = cue_POavg_n170_DFR_anova)
print(summary(cue_POavg_n170_LE.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 7.8 3.901 1.456 0.236
## Residuals 158 423.2 2.679
probe_POavg_n170_LE.aov <- aov(load_effect ~ level, data = probe_POavg_n170_DFR_anova)
print(summary(probe_POavg_n170_LE.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 39 19.59 0.677 0.51
## Residuals 158 4571 28.93
cue_P3_LE.aov <- aov(load_effect ~ level, data = cue_P3_DFR_anova)
print(summary(cue_P3_LE.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.62 0.3077 0.181 0.835
## Residuals 158 268.74 1.7009
probe_P3_LE.aov <- aov(load_effect ~ level, data = probe_P3_DFR_anova)
print(summary(probe_P3_LE.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.3 0.1252 0.046 0.955
## Residuals 158 428.7 2.7134
temp_diff <- constructs_fMRI[,c(1,8)]
omnibus_span_corr <- list(
ERSPS_midOccip_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_midOccip_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_midOccip_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Oz_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Oz_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Oz_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Favg_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Favg_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Favg_DFR[["PTID"]], times=ERSPS_times_DFR)
)
## Warning: `as_tibble.matrix()` requires a matrix with column names or a `.name_repair` argument. Using compatibility `.name_repair`.
## This warning is displayed once per session.
temp_diff <- p200_data[p200_data$PTID %in% constructs_fMRI$PTID, c(1,7)]
DFR_L3_acc_corr <- list(
ERSPS_midOccip_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_midOccip_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_midOccip_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Oz_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Oz_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Oz_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Favg_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Favg_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Favg_DFR[["PTID"]], times=ERSPS_times_DFR)
)
temp_diff <- data.frame(PTID=constructs_fMRI$PTID, LE = p200_delay_DFR$DFR_Load3_Load1)
DFR_fMRI_LE_corr <- list(
ERSPS_midOccip_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_midOccip_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_midOccip_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Oz_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Oz_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Oz_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Favg_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Favg_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Favg_DFR[["PTID"]], times=ERSPS_times_DFR)
)
avg_ERSPS_midOccip_DFR <- mutate_for_heatmap(apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]],c(1,2),mean))
avg_ERSPS_Oz_DFR <- mutate_for_heatmap(apply(ERSPS_Oz_DFR[["data"]][["load_effect"]],c(1,2),mean))
avg_ERSPS_Favg_DFR <- mutate_for_heatmap(apply(ERSPS_Favg_DFR[["data"]][["load_effect"]],c(1,2),mean))
ggplot(data=avg_ERSPS_midOccip_DFR, aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic()
ggplot(data=avg_ERSPS_Oz_DFR, aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic()
ggplot(data=avg_ERSPS_Favg_DFR, aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("F avg electrodes during DFR")+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic()
These plots give a good overview, but we will inspect each of the frequency bands more in depth later.
split_ERSPS_midOccip_DFR <- split_spectrum(ERSPS_midOccip_DFR, WM_groups)
split_ERSPS_Oz_DFR <- split_spectrum(ERSPS_Oz_DFR, WM_groups)
split_ERSPS_Favg_DFR <- split_spectrum(ERSPS_Favg_DFR, WM_groups)
split_ERSPS_midOccip_DFR_for_plot <- list()
split_ERSPS_Oz_DFR_for_plot <- list()
split_ERSPS_Favg_DFR_for_plot <- list()
split_ERSPS_midOccip_DFR_plots <- list()
split_ERSPS_Oz_DFR_plots <- list()
split_ERSPS_Favg_DFR_plots <- list()
for (group in c("high","med","low")){
split_ERSPS_midOccip_DFR_for_plot[[group]] <- mutate_for_heatmap(split_ERSPS_midOccip_DFR[[group]][["load_effect"]])
split_ERSPS_Oz_DFR_for_plot[[group]] <- mutate_for_heatmap(split_ERSPS_Oz_DFR[[group]][["load_effect"]])
split_ERSPS_Favg_DFR_for_plot[[group]] <- mutate_for_heatmap(split_ERSPS_Favg_DFR[[group]][["load_effect"]])
split_ERSPS_midOccip_DFR_plots[[group]] <- ggplot(data=split_ERSPS_midOccip_DFR_for_plot[[group]], aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle(paste0(group, " WMC in midOccip during DFR"))+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic()
split_ERSPS_Oz_DFR_plots[[group]] <- ggplot(data=split_ERSPS_Oz_DFR_for_plot[[group]], aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle(paste0(group, " WMC in Oz during DFR"))+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic()
split_ERSPS_Favg_DFR_plots[[group]] <- ggplot(data=split_ERSPS_Favg_DFR_for_plot[[group]], aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle(paste0(group, " WMC in Favg during DFR"))+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic()
}
split_ERSPS_midOccip_DFR_plots[["high"]]
split_ERSPS_midOccip_DFR_plots[["med"]]
split_ERSPS_midOccip_DFR_plots[["low"]]
split_ERSPS_Oz_DFR_plots[["high"]]
split_ERSPS_Oz_DFR_plots[["med"]]
split_ERSPS_Oz_DFR_plots[["low"]]
split_ERSPS_Favg_DFR_plots[["high"]]
split_ERSPS_Favg_DFR_plots[["med"]]
split_ERSPS_Favg_DFR_plots[["low"]]
Mid occipital cluster shows a positive correlation with span during the encoding and delay period in the beta and low gamma bands, and a negative correlation in the theta through alpha bands during the entire task, but most prominently right at the beginning of the probe period.
Oz shows a strong positive correlation with alpha power during encoding and what looks to be low gamma across the entire task. There also seems to be a negative correlation with beta across the entire task, with particularly strong negative correlations right at the end of the delay period/beginning of probe period.
F avg shows correlations during encoding with the theta band and low gamma/high beta that drift into the beginning of delay, and then a strong negative correlation right at the end of the delay period/beginning of the probe period.
ggplot(data=omnibus_span_corr[["ERSPS_midOccip_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=omnibus_span_corr[["ERSPS_Oz_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=omnibus_span_corr[["ERSPS_Favg_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("F avg electrodes during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=DFR_L3_acc_corr[["ERSPS_midOccip_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=DFR_L3_acc_corr[["ERSPS_Oz_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=DFR_L3_acc_corr[["ERSPS_Favg_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("F avg electrodes during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
There seems to be a strong correlation between fMRI load effect and power in the alpha and beta band across electrodes, particularly during encoding.
ggplot(data=DFR_fMRI_LE_corr[["ERSPS_midOccip_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=DFR_fMRI_LE_corr[["ERSPS_Oz_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=DFR_fMRI_LE_corr[["ERSPS_Favg_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("F avg electrodes during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
alpha <- list(
ERSPS_midOccip_DFR = list(
high_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["high_load"]][26:38,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["low_load"]][26:38,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]][26:38,,], c(2,3), mean)))),
ERSPS_Favg_DFR = list(
high_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["high_load"]][26:38,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["low_load"]][26:38,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["load_effect"]][26:38,,], c(2,3), mean)))),
ERSPS_Oz_DFR = list(
high_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["high_load"]][26:38,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["low_load"]][26:38,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["load_effect"]][26:38,,], c(2,3), mean))))
)
beta <- list(
ERSPS_midOccip_DFR = list(
high_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["high_load"]][37:45,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["low_load"]][37:45,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]][37:45,,], c(2,3), mean)))),
ERSPS_Favg_DFR = list(
high_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["high_load"]][37:45,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["low_load"]][37:45,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["load_effect"]][37:45,,], c(2,3), mean)))),
ERSPS_Oz_DFR = list(
high_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["high_load"]][37:45,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["low_load"]][37:45,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["load_effect"]][37:45,,], c(2,3), mean))))
)
theta <- list(
ERSPS_midOccip_DFR = list(
high_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["high_load"]][1:25,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["low_load"]][1:25,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]][1:25,,], c(2,3), mean)))),
ERSPS_Favg_DFR = list(
high_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["high_load"]][1:25,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["low_load"]][1:25,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["load_effect"]][1:25,,], c(2,3), mean)))),
ERSPS_Oz_DFR = list(
high_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["high_load"]][1:25,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["low_load"]][1:25,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["load_effect"]][1:25,,], c(2,3), mean))))
)
low_gamma <- list(
ERSPS_midOccip_DFR = list(
high_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["high_load"]][61:75,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["low_load"]][61:75,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]][61:75,,], c(2,3), mean)))),
ERSPS_Favg_DFR = list(
high_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["high_load"]][61:75,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["low_load"]][61:75,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["load_effect"]][61:75,,], c(2,3), mean)))),
ERSPS_Oz_DFR = list(
high_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["high_load"]][61:75,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["low_load"]][61:75,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["load_effect"]][61:75,,], c(2,3), mean))))
)
alpha_list <- list(
ERSPS_midOccip_DFR = data.frame(
high_load = apply(alpha[["ERSPS_midOccip_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(alpha[["ERSPS_midOccip_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(alpha[["ERSPS_midOccip_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Oz_DFR = data.frame(
high_load = apply(alpha[["ERSPS_Oz_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(alpha[["ERSPS_Oz_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(alpha[["ERSPS_Oz_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Favg_DFR = data.frame(
high_load = apply(alpha[["ERSPS_Favg_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(alpha[["ERSPS_Favg_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(alpha[["ERSPS_Favg_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR)
)
beta_list <- list(
ERSPS_midOccip_DFR = data.frame(
high_load = apply(beta[["ERSPS_midOccip_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(beta[["ERSPS_midOccip_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(beta[["ERSPS_midOccip_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Oz_DFR = data.frame(
high_load = apply(beta[["ERSPS_Oz_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(beta[["ERSPS_Oz_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(beta[["ERSPS_Oz_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Favg_DFR = data.frame(
high_load = apply(beta[["ERSPS_Favg_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(beta[["ERSPS_Favg_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(beta[["ERSPS_Favg_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR)
)
theta_list <- list(
ERSPS_midOccip_DFR = data.frame(
high_load = apply(theta[["ERSPS_midOccip_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(theta[["ERSPS_midOccip_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(theta[["ERSPS_midOccip_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Oz_DFR = data.frame(
high_load = apply(theta[["ERSPS_Oz_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(theta[["ERSPS_Oz_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(theta[["ERSPS_Oz_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Favg_DFR = data.frame(
high_load = apply(theta[["ERSPS_Favg_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(theta[["ERSPS_Favg_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(theta[["ERSPS_Favg_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR)
)
low_gamma_list <- list(
ERSPS_midOccip_DFR = data.frame(
high_load = apply(low_gamma[["ERSPS_midOccip_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(low_gamma[["ERSPS_midOccip_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(low_gamma[["ERSPS_midOccip_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Oz_DFR = data.frame(
high_load = apply(low_gamma[["ERSPS_Oz_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(low_gamma[["ERSPS_Oz_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(low_gamma[["ERSPS_Oz_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Favg_DFR = data.frame(
high_load = apply(low_gamma[["ERSPS_Favg_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(low_gamma[["ERSPS_Favg_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(low_gamma[["ERSPS_Favg_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR)
)
alpha_plot_list <- list()
beta_plot_list <- list()
theta_plot_list <- list()
low_gamma_plot_list <- list()
for (cluster in seq.int(1,length(alpha))){
alpha_plot_list[[names(alpha_list)[cluster]]][["indiv_loads"]] <- ggplot(data = alpha_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=high_load))+
geom_line(aes(x=time,y=low_load),linetype="dotted")+
ylab("alpha power")+
ggtitle("Low Load vs High Load")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
alpha_plot_list[[names(alpha_list)[cluster]]][["LE"]] <- ggplot(data = alpha_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=load_effect))+
ylab("alpha power")+
ggtitle("Load Effect")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
}
for (cluster in seq.int(1,length(beta))){
beta_plot_list[[names(beta_list)[cluster]]][["indiv_loads"]] <- ggplot(data = beta_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=high_load))+
geom_line(aes(x=time,y=low_load),linetype="dotted")+
ylab("beta power")+
ggtitle("Low Load vs High Load")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
beta_plot_list[[names(beta_list)[cluster]]][["LE"]] <- ggplot(data = beta_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=load_effect))+
ylab("beta power")+
ggtitle("Load Effect")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
}
for (cluster in seq.int(1,length(theta))){
theta_plot_list[[names(theta_list)[cluster]]][["indiv_loads"]] <- ggplot(data = theta_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=high_load))+
geom_line(aes(x=time,y=low_load),linetype="dotted")+
ylab("theta power")+
ggtitle("Low Load vs High Load")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
theta_plot_list[[names(theta_list)[cluster]]][["LE"]] <- ggplot(data = theta_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=load_effect))+
ylab("theta power")+
ggtitle("Load Effect")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
}
for (cluster in seq.int(1,length(low_gamma))){
low_gamma_plot_list[[names(low_gamma_list)[cluster]]][["indiv_loads"]] <- ggplot(data = low_gamma_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=high_load))+
geom_line(aes(x=time,y=low_load),linetype="dotted")+
ylab("low_gamma power")+
ggtitle("Low Load vs High Load")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
low_gamma_plot_list[[names(low_gamma_list)[cluster]]][["LE"]] <- ggplot(data = low_gamma_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=load_effect))+
ylab("low_gamma power")+
ggtitle("Load Effect")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
}
When we look at the load effects over all subjects, we see significant load effects in the alpha band during the cue and probe period in Oz, mid Occipital cluster and F average electrodes and during the delay period in the Oz electrode. During the cue and probe periods, we see stronger power in the low load, but we see the opposite effect in the delay period.
alpha_plot_list[["ERSPS_midOccip_DFR"]][["indiv_loads"]] + alpha_plot_list[["ERSPS_midOccip_DFR"]][["LE"]] +
plot_annotation(title="midOccip - alpha during DFR")
alpha_plot_list[["ERSPS_Oz_DFR"]][["indiv_loads"]] + alpha_plot_list[["ERSPS_Oz_DFR"]][["LE"]] +
plot_annotation(title="Oz - alpha during DFR")
alpha_plot_list[["ERSPS_Favg_DFR"]][["indiv_loads"]] + alpha_plot_list[["ERSPS_Favg_DFR"]][["LE"]] +
plot_annotation(title="F avg - alpha during DFR")
alpha_cue_average_midOccip <- select_period_average(alpha[["ERSPS_midOccip_DFR"]],0,2531.25,ERSPS_times_DFR)
alpha_cue_average_Oz <- select_period_average(alpha[["ERSPS_Oz_DFR"]],0,2531.25,ERSPS_times_DFR)
alpha_cue_average_Favg <- select_period_average(alpha[["ERSPS_Favg_DFR"]],0,2531.25,ERSPS_times_DFR)
alpha_delay_average_midOccip <- select_period_average(alpha[["ERSPS_midOccip_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
alpha_delay_average_Oz <- select_period_average(alpha[["ERSPS_Oz_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
alpha_delay_average_Favg <- select_period_average(alpha[["ERSPS_Favg_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
alpha_probe_average_midOccip <- select_period_average(alpha[["ERSPS_midOccip_DFR"]],5511.71875,7000,ERSPS_times_DFR)
alpha_probe_average_Oz <- select_period_average(alpha[["ERSPS_Oz_DFR"]],5511.71875,7000,ERSPS_times_DFR)
alpha_probe_average_Favg <- select_period_average(alpha[["ERSPS_Favg_DFR"]],5511.71875,7000,ERSPS_times_DFR)
t.test(alpha_cue_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: alpha_cue_average_midOccip$load_effect
## t = -8.7699, df = 177, p-value = 1.454e-15
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8545668 -0.5406145
## sample estimates:
## mean of x
## -0.6975907
t.test(alpha_cue_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: alpha_cue_average_Oz$load_effect
## t = -4.4787, df = 189, p-value = 1.298e-05
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.4582411 -0.1780102
## sample estimates:
## mean of x
## -0.3181257
t.test(alpha_cue_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: alpha_cue_average_Favg$load_effect
## t = -4.4186, df = 189, p-value = 1.671e-05
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -2.5142775 -0.9622461
## sample estimates:
## mean of x
## -1.738262
t.test(alpha_delay_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: alpha_delay_average_midOccip$load_effect
## t = -0.1686, df = 177, p-value = 0.8663
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.1825836 0.1538409
## sample estimates:
## mean of x
## -0.01437135
t.test(alpha_delay_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: alpha_delay_average_Oz$load_effect
## t = 2.3001, df = 189, p-value = 0.02253
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.02393801 0.31225787
## sample estimates:
## mean of x
## 0.1680979
t.test(alpha_delay_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: alpha_delay_average_Favg$load_effect
## t = -0.33285, df = 189, p-value = 0.7396
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.0635197 0.7564286
## sample estimates:
## mean of x
## -0.1535456
t.test(alpha_probe_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: alpha_probe_average_midOccip$load_effect
## t = -3.6404, df = 177, p-value = 0.0003572
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.6577849 -0.1953179
## sample estimates:
## mean of x
## -0.4265514
t.test(alpha_probe_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: alpha_probe_average_Oz$load_effect
## t = -5.941, df = 189, p-value = 1.338e-08
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.7887671 -0.3955406
## sample estimates:
## mean of x
## -0.5921539
t.test(alpha_probe_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: alpha_probe_average_Favg$load_effect
## t = -5.2797, df = 189, p-value = 3.533e-07
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -6.133077 -2.796738
## sample estimates:
## mean of x
## -4.464908
alphas_for_plot <- list()
betas_for_plot <- list()
thetas_for_plot <- list()
low_gammas_for_plot <- list()
load_list <- c("low_load", "high_load", "load_effect")
ERSPS_list <- list(
ERSPS_midOccip_DFR = ERSPS_midOccip_DFR,
ERSPS_Oz_DFR = ERSPS_Oz_DFR,
ERSPS_Favg_DFR = ERSPS_Favg_DFR)
for (cluster in seq.int(1,3)){
temp <- list()
for (load in seq.int(1,3)){
temp[[load_list[load]]] <- alpha[[cluster]][[load]]
}
if (substr(names(alpha)[cluster],nchar(names(alpha)[cluster])-4,nchar(names(alpha)[cluster])-2) == "LCD"){
alphas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups, ERSPS_times_LCD)
} else{
alphas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups, ERSPS_times_DFR)
}
}
for (cluster in seq.int(1,3)){
temp <- list()
for (load in seq.int(1,3)){
temp[[load_list[load]]] <- beta[[cluster]][[load]]
}
if (substr(names(beta)[cluster],nchar(names(beta)[cluster])-4,nchar(names(beta)[cluster])-2) == "LCD"){
betas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups, ERSPS_times_LCD)
} else{
betas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups, ERSPS_times_DFR)
}
}
for (cluster in seq.int(1,3)){
temp <- list()
for (load in seq.int(1,3)){
temp[[load_list[load]]] <- theta[[cluster]][[load]]
}
if (substr(names(theta)[cluster],nchar(names(theta)[cluster])-4,nchar(names(theta)[cluster])-2) == "LCD"){
thetas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups, ERSPS_times_LCD)
} else{
thetas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups, ERSPS_times_DFR)
}
}
for (cluster in seq.int(1,3)){
temp <- list()
for (load in seq.int(1,3)){
temp[[load_list[load]]] <- low_gamma[[cluster]][[load]]
}
if (substr(names(low_gamma)[cluster],nchar(names(low_gamma)[cluster])-4,nchar(names(low_gamma)[cluster])-2) == "LCD"){
low_gammas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups, ERSPS_times_LCD)
} else{
low_gammas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups, ERSPS_times_DFR)
}
}
When we split into the working memory capacity groups, we see differences across capacity in the mid occipital cluster during probe period, where we see a more negative load effect in the high capacity group vs the medium capacity group. We also see a significant difference between the low and high capacity subjects in the raw high load power, where the high capacity subjects show more negative power than the low load subjects.
We also can see an inverted U shape during the delay period in the Oz electrode, though it isn’t significant.
split_alphas_plot <- create_TC_for_plot(alphas_for_plot)
split_betas_plot <- create_TC_for_plot(betas_for_plot)
split_thetas_plot <- create_TC_for_plot(thetas_for_plot)
split_low_gammas_plot <- create_TC_for_plot(low_gammas_for_plot)
midOccip_DFR_alpha <- paired_freq_plot(split_alphas_plot[["ERSPS_midOccip_DFR"]][["long"]], c("high","low", "load_effect"))
Oz_DFR_alpha <- paired_freq_plot(split_alphas_plot[["ERSPS_Oz_DFR"]][["long"]], c("high","low", "load_effect"))
Favg_DFR_alpha <- paired_freq_plot(split_alphas_plot[["ERSPS_Favg_DFR"]][["long"]], c("high","low", "load_effect"))
midOccip_DFR_alpha[[1]] + midOccip_DFR_alpha[[2]]+
plot_annotation(title="midOccip - alpha during DFR")+
plot_layout(guides="collect")
Oz_DFR_alpha[[1]] + Oz_DFR_alpha[[2]]+
plot_annotation(title="Oz - alpha during DFR")+
plot_layout(guides="collect")
Favg_DFR_alpha[[1]] + Favg_DFR_alpha[[2]]+
plot_annotation(title="F avg electrodes - alpha during DFR")+
plot_layout(guides="collect")
alpha_cue_Oz_DFR_split <- split_into_groups(alpha_cue_average_Oz, WM_groups)
alpha_cue_midOccip_DFR_split <- split_into_groups(alpha_cue_average_midOccip, WM_groups)
alpha_cue_Favg_DFR_split <- split_into_groups(alpha_cue_average_Favg, WM_groups)
alpha_delay_Oz_DFR_split <- split_into_groups(alpha_delay_average_Oz, WM_groups)
alpha_delay_midOccip_DFR_split <- split_into_groups(alpha_delay_average_midOccip, WM_groups)
alpha_delay_Favg_DFR_split <- split_into_groups(alpha_delay_average_Favg, WM_groups)
alpha_probe_Oz_DFR_split <- split_into_groups(alpha_probe_average_Oz, WM_groups)
alpha_probe_midOccip_DFR_split <- split_into_groups(alpha_probe_average_midOccip, WM_groups)
alpha_probe_Favg_DFR_split <- split_into_groups(alpha_probe_average_Favg, WM_groups)
alpha_cue_Oz_DFR_split_prepped <- prep_split_for_bar_plots(alpha_cue_Oz_DFR_split)
alpha_cue_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(alpha_cue_midOccip_DFR_split)
alpha_cue_Favg_DFR_split_prepped <- prep_split_for_bar_plots(alpha_cue_Favg_DFR_split)
alpha_delay_Oz_DFR_split_prepped <- prep_split_for_bar_plots(alpha_delay_Oz_DFR_split)
alpha_delay_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(alpha_delay_midOccip_DFR_split)
alpha_delay_Favg_DFR_split_prepped <- prep_split_for_bar_plots(alpha_delay_Favg_DFR_split)
alpha_probe_Oz_DFR_split_prepped <- prep_split_for_bar_plots(alpha_probe_Oz_DFR_split)
alpha_probe_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(alpha_probe_midOccip_DFR_split)
alpha_probe_Favg_DFR_split_prepped <- prep_split_for_bar_plots(alpha_probe_Favg_DFR_split)
alpha_cue_midOccip_DFR_anova <- merge(alpha_cue_average_midOccip,WM_to_merge, by="PTID")
alpha_delay_midOccip_DFR_anova <- merge(alpha_delay_average_midOccip,WM_to_merge, by="PTID")
alpha_probe_midOccip_DFR_anova <- merge(alpha_probe_average_midOccip,WM_to_merge, by="PTID")
alpha_cue_Oz_DFR_anova <- merge(alpha_cue_average_Oz,WM_to_merge, by="PTID")
alpha_delay_Oz_DFR_anova <- merge(alpha_delay_average_Oz,WM_to_merge, by="PTID")
alpha_probe_Oz_DFR_anova <- merge(alpha_probe_average_Oz,WM_to_merge, by="PTID")
alpha_cue_Favg_DFR_anova <- merge(alpha_cue_average_Favg,WM_to_merge, by="PTID")
alpha_delay_Favg_DFR_anova <- merge(alpha_delay_average_Favg,WM_to_merge, by="PTID")
alpha_probe_Favg_DFR_anova <- merge(alpha_probe_average_Favg,WM_to_merge, by="PTID")
alpha_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_cue_Oz_high
alpha_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_cue_midOccip_high
alpha_cue_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_cue_Favg_high
alpha_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_delay_Oz_high
alpha_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_delay_midOccip_high
alpha_delay_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_delay_Favg_high
alpha_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_probe_Oz_high
alpha_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_probe_midOccip_high
alpha_probe_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_probe_Favg_high
alpha_cue_Oz_high + alpha_delay_Oz_high + alpha_probe_Oz_high+
plot_annotation(title="Oz electrode alpha frequency in high load trials during DFR")
alpha_cue_Favg_high + alpha_delay_Favg_high + alpha_probe_Favg_high+
plot_annotation(title="F avg electrodes alpha frequency high load trials during DFR")
alpha_cue_midOccip_high + alpha_delay_midOccip_high + alpha_probe_midOccip_high+
plot_annotation(title="midOccip cluster alpha frequency high load trials during DFR")
alpha_cue_midOccip.aov <- aov(high_load ~ level, data = alpha_cue_midOccip_DFR_anova)
print("alpha - cue midOccip")
## [1] "alpha - cue midOccip"
print(summary(alpha_cue_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 4 1.984 0.757 0.471
## Residuals 148 388 2.622
alpha_delay_midOccip.aov <- aov(high_load ~ level, data = alpha_delay_midOccip_DFR_anova)
print("alpha - delay midOccip")
## [1] "alpha - delay midOccip"
print(summary(alpha_delay_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 8.7 4.350 1.338 0.265
## Residuals 148 481.1 3.251
alpha_probe_midOccip.aov <- aov(high_load ~ level, data = alpha_probe_midOccip_DFR_anova)
print("alpha - probe midOccip")
## [1] "alpha - probe midOccip"
print(summary(alpha_probe_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 6.71 3.357 3.084 0.0487 *
## Residuals 148 161.11 1.089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print(TukeyHSD(alpha_probe_midOccip.aov))
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = high_load ~ level, data = alpha_probe_midOccip_DFR_anova)
##
## $level
## diff lwr upr p adj
## low-high 0.5025709 0.01039164 0.9947501 0.0441720
## med-high 0.1361373 -0.35084905 0.6231237 0.7859313
## med-low -0.3664335 -0.86558064 0.1327136 0.1945464
alpha_cue_Oz.aov <- aov(high_load ~ level, data = alpha_cue_Oz_DFR_anova)
print("alpha - cue Oz")
## [1] "alpha - cue Oz"
print(summary(alpha_cue_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 10.7 5.355 2.274 0.106
## Residuals 158 372.1 2.355
alpha_delay_Oz.aov <- aov(high_load ~ level, data = alpha_delay_Oz_DFR_anova)
print("alpha - delay Oz")
## [1] "alpha - delay Oz"
print(summary(alpha_delay_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.2 0.0926 0.03 0.97
## Residuals 158 484.9 3.0689
alpha_probe_Oz.aov <- aov(high_load ~ level, data = alpha_probe_Oz_DFR_anova)
print("alpha - probe Oz")
## [1] "alpha - probe Oz"
print(summary(alpha_probe_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.05 0.5256 0.321 0.726
## Residuals 158 258.37 1.6353
alpha_cue_Favg.aov <- aov(high_load ~ level, data = alpha_cue_Favg_DFR_anova)
print("alpha - cue Favg")
## [1] "alpha - cue Favg"
print(summary(alpha_cue_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.51 1.757 0.884 0.415
## Residuals 158 314.14 1.988
alpha_delay_Favg.aov <- aov(high_load ~ level, data = alpha_delay_Favg_DFR_anova)
print("alpha - delay Favg")
## [1] "alpha - delay Favg"
print(summary(alpha_delay_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1 0.520 0.152 0.859
## Residuals 158 540 3.418
alpha_probe_Favg.aov <- aov(high_load ~ level, data = alpha_probe_Favg_DFR_anova)
print("alpha - probe Favg")
## [1] "alpha - probe Favg"
print(summary(alpha_probe_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.01 0.5069 0.447 0.64
## Residuals 158 179.27 1.1346
alpha_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_cue_Oz_LE
alpha_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_cue_midOccip_LE
alpha_cue_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_cue_Favg_LE
alpha_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_delay_Oz_LE
alpha_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_delay_midOccip_LE
alpha_delay_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_delay_Favg_LE
alpha_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_probe_Oz_LE
alpha_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_probe_midOccip_LE
alpha_probe_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_probe_Favg_LE
alpha_cue_Oz_LE + alpha_delay_Oz_LE + alpha_probe_Oz_LE+
plot_annotation(title="Oz electrode alpha frequency Load Effect during DFR")
alpha_cue_Favg_LE + alpha_delay_Favg_LE + alpha_probe_Favg_LE+
plot_annotation(title="F avg electrodes alpha frequency Load Effect during DFR")
alpha_cue_midOccip_LE + alpha_delay_midOccip_LE + alpha_probe_midOccip_LE+
plot_annotation(title="midOccip cluster alpha frequency Load Effect during DFR")
alpha_cue_midOccip.aov <- aov(load_effect ~ level, data = alpha_cue_midOccip_DFR_anova)
print("alpha - cue midOccip")
## [1] "alpha - cue midOccip"
print(summary(alpha_cue_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.99 1.494 1.31 0.273
## Residuals 148 168.85 1.141
alpha_delay_midOccip.aov <- aov(load_effect ~ level, data = alpha_delay_midOccip_DFR_anova)
print("alpha - delay midOccip")
## [1] "alpha - delay midOccip"
print(summary(alpha_delay_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.13 1.065 0.808 0.448
## Residuals 148 194.95 1.317
alpha_probe_midOccip.aov <- aov(load_effect ~ level, data = alpha_probe_midOccip_DFR_anova)
print("alpha - probe midOccip")
## [1] "alpha - probe midOccip"
print(summary(alpha_probe_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 16.0 8.019 3.094 0.0483 *
## Residuals 148 383.6 2.592
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print(TukeyHSD(alpha_probe_midOccip.aov))
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = load_effect ~ level, data = alpha_probe_midOccip_DFR_anova)
##
## $level
## diff lwr upr p adj
## low-high 0.6592005 -0.10025431 1.4186553 0.1029359
## med-high 0.7034747 -0.04796732 1.4549167 0.0717120
## med-low 0.0442742 -0.72593234 0.8144807 0.9898416
alpha_cue_Oz.aov <- aov(load_effect ~ level, data = alpha_cue_Oz_DFR_anova)
print("alpha - cue Oz")
## [1] "alpha - cue Oz"
print(summary(alpha_cue_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.65 0.8270 0.899 0.409
## Residuals 158 145.27 0.9194
alpha_delay_Oz.aov <- aov(load_effect ~ level, data = alpha_delay_Oz_DFR_anova)
print("alpha - delay Oz")
## [1] "alpha - delay Oz"
print(summary(alpha_delay_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.15 1.073 1.063 0.348
## Residuals 158 159.52 1.010
alpha_probe_Oz.aov <- aov(load_effect ~ level, data = alpha_probe_Oz_DFR_anova)
print("alpha - probe Oz")
## [1] "alpha - probe Oz"
print(summary(alpha_probe_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 7.76 3.881 2.218 0.112
## Residuals 158 276.44 1.750
alpha_cue_Favg.aov <- aov(load_effect ~ level, data = alpha_cue_Favg_DFR_anova)
print("alpha - cue Favg")
## [1] "alpha - cue Favg"
print(summary(alpha_cue_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 23 11.51 0.404 0.669
## Residuals 158 4504 28.51
alpha_delay_Favg.aov <- aov(load_effect ~ level, data = alpha_delay_Favg_DFR_anova)
print("alpha - delay Favg")
## [1] "alpha - delay Favg"
print(summary(alpha_delay_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 107 53.28 1.258 0.287
## Residuals 158 6690 42.34
alpha_probe_Favg.aov <- aov(load_effect ~ level, data = alpha_probe_Favg_DFR_anova)
print("alpha - probe Favg")
## [1] "alpha - probe Favg"
print(summary(alpha_probe_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 704 351.9 2.402 0.0939 .
## Residuals 158 23149 146.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Overall, we see differences in load effects during cue and probe in all electrodes (low > high) and in delay during mid occipital and Oz (high > low).
beta_plot_list[["ERSPS_midOccip_DFR"]][["indiv_loads"]] + beta_plot_list[["ERSPS_midOccip_DFR"]][["LE"]] +
plot_annotation(title="midOccip - beta during DFR")
beta_plot_list[["ERSPS_Oz_DFR"]][["indiv_loads"]] + beta_plot_list[["ERSPS_Oz_DFR"]][["LE"]] +
plot_annotation(title="Oz - beta during DFR")
beta_plot_list[["ERSPS_Favg_DFR"]][["indiv_loads"]] + beta_plot_list[["ERSPS_Favg_DFR"]][["LE"]] +
plot_annotation(title="F avg - beta during DFR")
beta_cue_average_midOccip <- select_period_average(beta[["ERSPS_midOccip_DFR"]],0,2531.25,ERSPS_times_DFR)
beta_cue_average_Oz <- select_period_average(beta[["ERSPS_Oz_DFR"]],0,2531.25,ERSPS_times_DFR)
beta_cue_average_Favg <- select_period_average(beta[["ERSPS_Favg_DFR"]],0,2531.25,ERSPS_times_DFR)
beta_delay_average_midOccip <- select_period_average(beta[["ERSPS_midOccip_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
beta_delay_average_Oz <- select_period_average(beta[["ERSPS_Oz_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
beta_delay_average_Favg <- select_period_average(beta[["ERSPS_Favg_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
beta_probe_average_midOccip <- select_period_average(beta[["ERSPS_midOccip_DFR"]],5511.71875,7000,ERSPS_times_DFR)
beta_probe_average_Oz <- select_period_average(beta[["ERSPS_Oz_DFR"]],5511.71875,7000,ERSPS_times_DFR)
beta_probe_average_Favg <- select_period_average(beta[["ERSPS_Favg_DFR"]],5511.71875,7000,ERSPS_times_DFR)
t.test(beta_cue_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: beta_cue_average_midOccip$load_effect
## t = -6.2428, df = 177, p-value = 3.092e-09
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.5050572 -0.2624377
## sample estimates:
## mean of x
## -0.3837475
t.test(beta_cue_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: beta_cue_average_Oz$load_effect
## t = -3.7009, df = 189, p-value = 0.0002817
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.3785452 -0.1153149
## sample estimates:
## mean of x
## -0.24693
t.test(beta_cue_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: beta_cue_average_Favg$load_effect
## t = -2.6149, df = 189, p-value = 0.009646
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.6410224 -0.2297544
## sample estimates:
## mean of x
## -0.9353884
t.test(beta_delay_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: beta_delay_average_midOccip$load_effect
## t = 2.1457, df = 177, p-value = 0.03326
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.01171767 0.28023834
## sample estimates:
## mean of x
## 0.145978
t.test(beta_delay_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: beta_delay_average_Oz$load_effect
## t = 6.7658, df = 189, p-value = 1.615e-10
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.3191722 0.5818761
## sample estimates:
## mean of x
## 0.4505242
t.test(beta_delay_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: beta_delay_average_Favg$load_effect
## t = 1.3947, df = 189, p-value = 0.1647
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.2261368 1.3176975
## sample estimates:
## mean of x
## 0.5457804
t.test(beta_probe_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: beta_probe_average_midOccip$load_effect
## t = -5.0094, df = 177, p-value = 1.315e-06
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.7269556 -0.3160592
## sample estimates:
## mean of x
## -0.5215074
t.test(beta_probe_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: beta_probe_average_Oz$load_effect
## t = -6.1079, df = 189, p-value = 5.63e-09
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.7840744 -0.4012606
## sample estimates:
## mean of x
## -0.5926675
t.test(beta_probe_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: beta_probe_average_Favg$load_effect
## t = -6.3629, df = 189, p-value = 1.459e-09
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -6.796846 -3.579886
## sample estimates:
## mean of x
## -5.188366
There are, however, no differences across groups in beta for either the high load trials or load effects.
midOccip_DFR_beta <- paired_freq_plot(split_betas_plot[["ERSPS_midOccip_DFR"]][["long"]], c("high","low", "load_effect"))
Oz_DFR_beta <- paired_freq_plot(split_betas_plot[["ERSPS_Oz_DFR"]][["long"]], c("high","low", "load_effect"))
Favg_DFR_beta <- paired_freq_plot(split_betas_plot[["ERSPS_Favg_DFR"]][["long"]], c("high","low", "load_effect"))
midOccip_DFR_beta[[1]] + midOccip_DFR_beta[[2]]+
plot_annotation(title="midOccip - beta during DFR")+
plot_layout(guides="collect")
Oz_DFR_beta[[1]] + Oz_DFR_beta[[2]]+
plot_annotation(title="Oz - beta during DFR")+
plot_layout(guides="collect")
Favg_DFR_beta[[1]] + Favg_DFR_beta[[2]]+
plot_annotation(title="F avg electrodes - beta during DFR")+
plot_layout(guides="collect")
beta_cue_Oz_DFR_split <- split_into_groups(beta_cue_average_Oz, WM_groups)
beta_cue_midOccip_DFR_split <- split_into_groups(beta_cue_average_midOccip, WM_groups)
beta_cue_Favg_DFR_split <- split_into_groups(beta_cue_average_Favg, WM_groups)
beta_delay_Oz_DFR_split <- split_into_groups(beta_delay_average_Oz, WM_groups)
beta_delay_midOccip_DFR_split <- split_into_groups(beta_delay_average_midOccip, WM_groups)
beta_delay_Favg_DFR_split <- split_into_groups(beta_delay_average_Favg, WM_groups)
beta_probe_Oz_DFR_split <- split_into_groups(beta_probe_average_Oz, WM_groups)
beta_probe_midOccip_DFR_split <- split_into_groups(beta_probe_average_midOccip, WM_groups)
beta_probe_Favg_DFR_split <- split_into_groups(beta_probe_average_Favg, WM_groups)
beta_cue_Oz_DFR_split_prepped <- prep_split_for_bar_plots(beta_cue_Oz_DFR_split)
beta_cue_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(beta_cue_midOccip_DFR_split)
beta_cue_Favg_DFR_split_prepped <- prep_split_for_bar_plots(beta_cue_Favg_DFR_split)
beta_delay_Oz_DFR_split_prepped <- prep_split_for_bar_plots(beta_delay_Oz_DFR_split)
beta_delay_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(beta_delay_midOccip_DFR_split)
beta_delay_Favg_DFR_split_prepped <- prep_split_for_bar_plots(beta_delay_Favg_DFR_split)
beta_probe_Oz_DFR_split_prepped <- prep_split_for_bar_plots(beta_probe_Oz_DFR_split)
beta_probe_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(beta_probe_midOccip_DFR_split)
beta_probe_Favg_DFR_split_prepped <- prep_split_for_bar_plots(beta_probe_Favg_DFR_split)
beta_cue_midOccip_DFR_anova <- merge(beta_cue_average_midOccip,WM_to_merge, by="PTID")
beta_delay_midOccip_DFR_anova <- merge(beta_delay_average_midOccip,WM_to_merge, by="PTID")
beta_probe_midOccip_DFR_anova <- merge(beta_probe_average_midOccip,WM_to_merge, by="PTID")
beta_cue_Oz_DFR_anova <- merge(beta_cue_average_Oz,WM_to_merge, by="PTID")
beta_delay_Oz_DFR_anova <- merge(beta_delay_average_Oz,WM_to_merge, by="PTID")
beta_probe_Oz_DFR_anova <- merge(beta_probe_average_Oz,WM_to_merge, by="PTID")
beta_cue_Favg_DFR_anova <- merge(beta_cue_average_Favg,WM_to_merge, by="PTID")
beta_delay_Favg_DFR_anova <- merge(beta_delay_average_Favg,WM_to_merge, by="PTID")
beta_probe_Favg_DFR_anova <- merge(beta_probe_average_Favg,WM_to_merge, by="PTID")
beta_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_cue_Oz_high
beta_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_cue_midOccip_high
beta_cue_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_cue_Favg_high
beta_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_delay_Oz_high
beta_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_delay_midOccip_high
beta_delay_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_delay_Favg_high
beta_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_probe_Oz_high
beta_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_probe_midOccip_high
beta_probe_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_probe_Favg_high
beta_cue_Oz_high + beta_delay_Oz_high + beta_probe_Oz_high+
plot_annotation(title="Oz electrode beta frequency in high load trials during DFR")
beta_cue_Favg_high + beta_delay_Favg_high + beta_probe_Favg_high+
plot_annotation(title="F avg electrodes beta frequency high load trials during DFR")
beta_cue_midOccip_high + beta_delay_midOccip_high + beta_probe_midOccip_high+
plot_annotation(title="midOccip cluster beta frequency high load trials during DFR")
beta_cue_midOccip.aov <- aov(high_load ~ level, data = beta_cue_midOccip_DFR_anova)
print("beta - cue midOccip")
## [1] "beta - cue midOccip"
print(summary(beta_cue_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 4.6 2.316 1.011 0.366
## Residuals 148 339.0 2.290
beta_delay_midOccip.aov <- aov(high_load ~ level, data = beta_delay_midOccip_DFR_anova)
print("beta - delay midOccip")
## [1] "beta - delay midOccip"
print(summary(beta_delay_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 4.06 2.029 1.294 0.277
## Residuals 148 232.03 1.568
beta_probe_midOccip.aov <- aov(high_load ~ level, data = beta_probe_midOccip_DFR_anova)
print("beta - probe midOccip")
## [1] "beta - probe midOccip"
print(summary(beta_probe_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 4.25 2.124 1.967 0.143
## Residuals 148 159.81 1.080
beta_cue_Oz.aov <- aov(high_load ~ level, data = beta_cue_Oz_DFR_anova)
print("beta - cue Oz")
## [1] "beta - cue Oz"
print(summary(beta_cue_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 9.1 4.552 2.184 0.116
## Residuals 158 329.4 2.085
beta_delay_Oz.aov <- aov(high_load ~ level, data = beta_delay_Oz_DFR_anova)
print("beta - delay Oz")
## [1] "beta - delay Oz"
print(summary(beta_delay_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.27 0.1365 0.075 0.928
## Residuals 158 287.05 1.8168
beta_probe_Oz.aov <- aov(high_load ~ level, data = beta_probe_Oz_DFR_anova)
print("beta - probe Oz")
## [1] "beta - probe Oz"
print(summary(beta_probe_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.34 1.171 0.635 0.531
## Residuals 158 291.37 1.844
beta_cue_Favg.aov <- aov(high_load ~ level, data = beta_cue_Favg_DFR_anova)
print("beta - cue Favg")
## [1] "beta - cue Favg"
print(summary(beta_cue_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.1 1.026 0.502 0.606
## Residuals 158 322.9 2.043
beta_delay_Favg.aov <- aov(high_load ~ level, data = beta_delay_Favg_DFR_anova)
print("beta - delay Favg")
## [1] "beta - delay Favg"
print(summary(beta_delay_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.16 0.0811 0.045 0.956
## Residuals 158 287.49 1.8195
beta_probe_Favg.aov <- aov(high_load ~ level, data = beta_probe_Favg_DFR_anova)
print("beta - probe Favg")
## [1] "beta - probe Favg"
print(summary(beta_probe_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 5.01 2.507 1.958 0.145
## Residuals 158 202.28 1.280
beta_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_cue_Oz_LE
beta_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_cue_midOccip_LE
beta_cue_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_cue_Favg_LE
beta_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_delay_Oz_LE
beta_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_delay_midOccip_LE
beta_delay_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_delay_Favg_LE
beta_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_probe_Oz_LE
beta_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_probe_midOccip_LE
beta_probe_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_probe_Favg_LE
beta_cue_Oz_LE + beta_delay_Oz_LE + beta_probe_Oz_LE+
plot_annotation(title="Oz electrode beta frequency Load Effect during DFR")
beta_cue_Favg_LE + beta_delay_Favg_LE + beta_probe_Favg_LE+
plot_annotation(title="F avg electrodes beta frequency Load Effect during DFR")
beta_cue_midOccip_LE + beta_delay_midOccip_LE + beta_probe_midOccip_LE+
plot_annotation(title="midOccip cluster beta frequency Load Effect during DFR")
beta_cue_midOccip.aov <- aov(load_effect ~ level, data = beta_cue_midOccip_DFR_anova)
print("beta - cue midOccip")
## [1] "beta - cue midOccip"
print(summary(beta_cue_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.15 0.0771 0.118 0.888
## Residuals 148 96.42 0.6515
beta_delay_midOccip.aov <- aov(load_effect ~ level, data = beta_delay_midOccip_DFR_anova)
print("beta - delay midOccip")
## [1] "beta - delay midOccip"
print(summary(beta_delay_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.09 0.0450 0.051 0.95
## Residuals 148 129.64 0.8759
beta_probe_midOccip.aov <- aov(load_effect ~ level, data = beta_probe_midOccip_DFR_anova)
print("beta - probe midOccip")
## [1] "beta - probe midOccip"
print(summary(beta_probe_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 8.04 4.020 1.886 0.155
## Residuals 148 315.42 2.131
beta_cue_Oz.aov <- aov(load_effect ~ level, data = beta_cue_Oz_DFR_anova)
print("beta - cue Oz")
## [1] "beta - cue Oz"
print(summary(beta_cue_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.46 0.2306 0.301 0.74
## Residuals 158 120.94 0.7655
beta_delay_Oz.aov <- aov(load_effect ~ level, data = beta_delay_Oz_DFR_anova)
print("beta - delay Oz")
## [1] "beta - delay Oz"
print(summary(beta_delay_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.59 0.7932 0.995 0.372
## Residuals 158 125.95 0.7971
beta_probe_Oz.aov <- aov(load_effect ~ level, data = beta_probe_Oz_DFR_anova)
print("beta - probe Oz")
## [1] "beta - probe Oz"
print(summary(beta_probe_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.04 1.020 0.589 0.556
## Residuals 158 273.47 1.731
beta_cue_Favg.aov <- aov(load_effect ~ level, data = beta_cue_Favg_DFR_anova)
print("beta - cue Favg")
## [1] "beta - cue Favg"
print(summary(beta_cue_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 23 11.43 0.506 0.604
## Residuals 158 3571 22.60
beta_delay_Favg.aov <- aov(load_effect ~ level, data = beta_delay_Favg_DFR_anova)
print("beta - delay Favg")
## [1] "beta - delay Favg"
print(summary(beta_delay_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 98 48.95 1.642 0.197
## Residuals 158 4711 29.81
beta_probe_Favg.aov <- aov(load_effect ~ level, data = beta_probe_Favg_DFR_anova)
print("beta - probe Favg")
## [1] "beta - probe Favg"
print(summary(beta_probe_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 499 249.4 1.786 0.171
## Residuals 158 22067 139.7
In the beta band, we can see significant load effects during the cue and delay periods in the Oz electrode (high load > low load), and in the probe period for the Oz electrode, mid Occipital cluster and F avg during the probe period (low > high).
theta_plot_list[["ERSPS_midOccip_DFR"]][["indiv_loads"]] + theta_plot_list[["ERSPS_midOccip_DFR"]][["LE"]] +
plot_annotation(title="midOccip - theta during DFR")
theta_plot_list[["ERSPS_Oz_DFR"]][["indiv_loads"]] + theta_plot_list[["ERSPS_Oz_DFR"]][["LE"]] +
plot_annotation(title="Oz - theta during DFR")
theta_plot_list[["ERSPS_Favg_DFR"]][["indiv_loads"]] + theta_plot_list[["ERSPS_Favg_DFR"]][["LE"]] +
plot_annotation(title="F avg - theta during DFR")
theta_cue_average_midOccip <- select_period_average(theta[["ERSPS_midOccip_DFR"]],0,2531.25,ERSPS_times_DFR)
theta_cue_average_Oz <- select_period_average(theta[["ERSPS_Oz_DFR"]],0,2531.25,ERSPS_times_DFR)
theta_cue_average_Favg <- select_period_average(theta[["ERSPS_Favg_DFR"]],0,2531.25,ERSPS_times_DFR)
theta_delay_average_midOccip <- select_period_average(theta[["ERSPS_midOccip_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
theta_delay_average_Oz <- select_period_average(theta[["ERSPS_Oz_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
theta_delay_average_Favg <- select_period_average(theta[["ERSPS_Favg_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
theta_probe_average_midOccip <- select_period_average(theta[["ERSPS_midOccip_DFR"]],5511.71875,7000,ERSPS_times_DFR)
theta_probe_average_Oz <- select_period_average(theta[["ERSPS_Oz_DFR"]],5511.71875,7000,ERSPS_times_DFR)
theta_probe_average_Favg <- select_period_average(theta[["ERSPS_Favg_DFR"]],5511.71875,7000,ERSPS_times_DFR)
t.test(theta_cue_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: theta_cue_average_midOccip$load_effect
## t = -0.4662, df = 177, p-value = 0.6416
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.1718366 0.1061636
## sample estimates:
## mean of x
## -0.03283653
t.test(theta_cue_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: theta_cue_average_Oz$load_effect
## t = 5.8367, df = 189, p-value = 2.28e-08
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.2575268 0.5204594
## sample estimates:
## mean of x
## 0.3889931
t.test(theta_cue_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: theta_cue_average_Favg$load_effect
## t = -1.6775, df = 189, p-value = 0.09509
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.05204117 0.08504174
## sample estimates:
## mean of x
## -0.4834997
t.test(theta_delay_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: theta_delay_average_midOccip$load_effect
## t = -0.21113, df = 177, p-value = 0.833
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.1921162 0.1549821
## sample estimates:
## mean of x
## -0.01856705
t.test(theta_delay_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: theta_delay_average_Oz$load_effect
## t = 2.9431, df = 189, p-value = 0.003656
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.07886558 0.39945721
## sample estimates:
## mean of x
## 0.2391614
t.test(theta_delay_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: theta_delay_average_Favg$load_effect
## t = -1.2367, df = 189, p-value = 0.2177
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.2826749 0.2941081
## sample estimates:
## mean of x
## -0.4942834
t.test(theta_probe_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: theta_probe_average_midOccip$load_effect
## t = -6.444, df = 177, p-value = 1.067e-09
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.1390326 -0.6049424
## sample estimates:
## mean of x
## -0.8719875
t.test(theta_probe_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: theta_probe_average_Oz$load_effect
## t = -6.7285, df = 189, p-value = 1.986e-10
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.0040782 -0.5488154
## sample estimates:
## mean of x
## -0.7764468
t.test(theta_probe_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: theta_probe_average_Favg$load_effect
## t = -6.7464, df = 189, p-value = 1.799e-10
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -7.908032 -4.329772
## sample estimates:
## mean of x
## -6.118902
We see significant differences between WMC groups during the delay period in the mid Occipital cluster (negative linear relationship) and the cue period in the Oz electrode (larger load effects in the high capacity subjects).
In the raw power at high load, we also see a difference in the delay period in the mid occipital cluster, where there is significantly more negative power in the high capacity subjects than the low capacity subjects.
midOccip_DFR_theta <- paired_freq_plot(split_thetas_plot[["ERSPS_midOccip_DFR"]][["long"]], c("high","low", "load_effect"))
Oz_DFR_theta <- paired_freq_plot(split_thetas_plot[["ERSPS_Oz_DFR"]][["long"]], c("high","low", "load_effect"))
Favg_DFR_theta <- paired_freq_plot(split_thetas_plot[["ERSPS_Favg_DFR"]][["long"]], c("high","low", "load_effect"))
midOccip_DFR_theta[[1]] + midOccip_DFR_theta[[2]]+
plot_annotation(title="midOccip - theta during DFR")+
plot_layout(guides="collect")
Oz_DFR_theta[[1]] + Oz_DFR_theta[[2]]+
plot_annotation(title="Oz - theta during DFR")+
plot_layout(guides="collect")
Favg_DFR_theta[[1]] + Favg_DFR_theta[[2]]+
plot_annotation(title="F avg electrodes - theta during DFR")+
plot_layout(guides="collect")
theta_cue_Oz_DFR_split <- split_into_groups(theta_cue_average_Oz, WM_groups)
theta_cue_midOccip_DFR_split <- split_into_groups(theta_cue_average_midOccip, WM_groups)
theta_cue_Favg_DFR_split <- split_into_groups(theta_cue_average_Favg, WM_groups)
theta_delay_Oz_DFR_split <- split_into_groups(theta_delay_average_Oz, WM_groups)
theta_delay_midOccip_DFR_split <- split_into_groups(theta_delay_average_midOccip, WM_groups)
theta_delay_Favg_DFR_split <- split_into_groups(theta_delay_average_Favg, WM_groups)
theta_probe_Oz_DFR_split <- split_into_groups(theta_probe_average_Oz, WM_groups)
theta_probe_midOccip_DFR_split <- split_into_groups(theta_probe_average_midOccip, WM_groups)
theta_probe_Favg_DFR_split <- split_into_groups(theta_probe_average_Favg, WM_groups)
theta_cue_Oz_DFR_split_prepped <- prep_split_for_bar_plots(theta_cue_Oz_DFR_split)
theta_cue_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(theta_cue_midOccip_DFR_split)
theta_cue_Favg_DFR_split_prepped <- prep_split_for_bar_plots(theta_cue_Favg_DFR_split)
theta_delay_Oz_DFR_split_prepped <- prep_split_for_bar_plots(theta_delay_Oz_DFR_split)
theta_delay_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(theta_delay_midOccip_DFR_split)
theta_delay_Favg_DFR_split_prepped <- prep_split_for_bar_plots(theta_delay_Favg_DFR_split)
theta_probe_Oz_DFR_split_prepped <- prep_split_for_bar_plots(theta_probe_Oz_DFR_split)
theta_probe_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(theta_probe_midOccip_DFR_split)
theta_probe_Favg_DFR_split_prepped <- prep_split_for_bar_plots(theta_probe_Favg_DFR_split)
theta_cue_midOccip_DFR_anova <- merge(theta_cue_average_midOccip,WM_to_merge, by="PTID")
theta_delay_midOccip_DFR_anova <- merge(theta_delay_average_midOccip,WM_to_merge, by="PTID")
theta_probe_midOccip_DFR_anova <- merge(theta_probe_average_midOccip,WM_to_merge, by="PTID")
theta_cue_Oz_DFR_anova <- merge(theta_cue_average_Oz,WM_to_merge, by="PTID")
theta_delay_Oz_DFR_anova <- merge(theta_delay_average_Oz,WM_to_merge, by="PTID")
theta_probe_Oz_DFR_anova <- merge(theta_probe_average_Oz,WM_to_merge, by="PTID")
theta_cue_Favg_DFR_anova <- merge(theta_cue_average_Favg,WM_to_merge, by="PTID")
theta_delay_Favg_DFR_anova <- merge(theta_delay_average_Favg,WM_to_merge, by="PTID")
theta_probe_Favg_DFR_anova <- merge(theta_probe_average_Favg,WM_to_merge, by="PTID")
theta_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_cue_Oz_high
theta_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_cue_midOccip_high
theta_cue_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_cue_Favg_high
theta_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_delay_Oz_high
theta_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_delay_midOccip_high
theta_delay_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_delay_Favg_high
theta_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_probe_Oz_high
theta_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_probe_midOccip_high
theta_probe_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_probe_Favg_high
theta_cue_Oz_high + theta_delay_Oz_high + theta_probe_Oz_high+
plot_annotation(title="Oz electrode theta frequency in high load trials during DFR")
theta_cue_Favg_high + theta_delay_Favg_high + theta_probe_Favg_high+
plot_annotation(title="F avg electrodes theta frequency high load trials during DFR")
theta_cue_midOccip_high + theta_delay_midOccip_high + theta_probe_midOccip_high+
plot_annotation(title="midOccip cluster theta frequency high load trials during DFR")
theta_cue_midOccip.aov <- aov(high_load ~ level, data = theta_cue_midOccip_DFR_anova)
print("theta - cue midOccip")
## [1] "theta - cue midOccip"
print(summary(theta_cue_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.92 0.4585 0.454 0.636
## Residuals 148 149.50 1.0101
theta_delay_midOccip.aov <- aov(high_load ~ level, data = theta_delay_midOccip_DFR_anova)
print("theta - delay midOccip")
## [1] "theta - delay midOccip"
print(summary(theta_delay_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 10.55 5.276 3.954 0.0212 *
## Residuals 148 197.47 1.334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print(TukeyHSD(theta_delay_midOccip.aov))
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = high_load ~ level, data = theta_delay_midOccip_DFR_anova)
##
## $level
## diff lwr upr p adj
## low-high 0.6453087 0.1004122 1.1902052 0.0157139
## med-high 0.2634821 -0.2756653 0.8026296 0.4807732
## med-low -0.3818266 -0.9344373 0.1707841 0.2338716
theta_probe_midOccip.aov <- aov(high_load ~ level, data = theta_probe_midOccip_DFR_anova)
print("theta - probe midOccip")
## [1] "theta - probe midOccip"
print(summary(theta_probe_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.77 0.8828 1.623 0.201
## Residuals 148 80.50 0.5439
theta_cue_Oz.aov <- aov(high_load ~ level, data = theta_cue_Oz_DFR_anova)
print("theta - cue Oz")
## [1] "theta - cue Oz"
print(summary(theta_cue_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 5.72 2.858 1.699 0.186
## Residuals 158 265.85 1.683
theta_delay_Oz.aov <- aov(high_load ~ level, data = theta_delay_Oz_DFR_anova)
print("theta - delay Oz")
## [1] "theta - delay Oz"
print(summary(theta_delay_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.94 0.9721 0.693 0.501
## Residuals 158 221.49 1.4018
theta_probe_Oz.aov <- aov(high_load ~ level, data = theta_probe_Oz_DFR_anova)
print("theta - probe Oz")
## [1] "theta - probe Oz"
print(summary(theta_probe_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 4.52 2.260 1.656 0.194
## Residuals 158 215.66 1.365
theta_cue_Favg.aov <- aov(high_load ~ level, data = theta_cue_Favg_DFR_anova)
print("theta - cue Favg")
## [1] "theta - cue Favg"
print(summary(theta_cue_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.42 1.2087 1.778 0.172
## Residuals 158 107.39 0.6797
theta_delay_Favg.aov <- aov(high_load ~ level, data = theta_delay_Favg_DFR_anova)
print("theta - delay Favg")
## [1] "theta - delay Favg"
print(summary(theta_delay_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.4 0.1992 0.227 0.797
## Residuals 158 138.8 0.8785
theta_probe_Favg.aov <- aov(high_load ~ level, data = theta_probe_Favg_DFR_anova)
print("theta - probe Favg")
## [1] "theta - probe Favg"
print(summary(theta_probe_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 4.27 2.1371 2.665 0.0727 .
## Residuals 158 126.69 0.8019
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
theta_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_cue_Oz_LE
theta_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_cue_midOccip_LE
theta_cue_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_cue_Favg_LE
theta_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_delay_Oz_LE
theta_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_delay_midOccip_LE
theta_delay_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_delay_Favg_LE
theta_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_probe_Oz_LE
theta_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_probe_midOccip_LE
theta_probe_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> theta_probe_Favg_LE
theta_cue_Oz_LE + theta_delay_Oz_LE + theta_probe_Oz_LE+
plot_annotation(title="Oz electrode theta frequency Load Effect during DFR")
theta_cue_Favg_LE + theta_delay_Favg_LE + theta_probe_Favg_LE+
plot_annotation(title="F avg electrodes theta frequency Load Effect during DFR")
theta_cue_midOccip_LE + theta_delay_midOccip_LE + theta_probe_midOccip_LE+
plot_annotation(title="midOccip cluster theta frequency Load Effect during DFR")
theta_cue_midOccip.aov <- aov(load_effect ~ level, data = theta_cue_midOccip_DFR_anova)
print("theta - cue midOccip")
## [1] "theta - cue midOccip"
print(summary(theta_cue_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.43 1.215 1.377 0.255
## Residuals 148 130.54 0.882
theta_delay_midOccip.aov <- aov(load_effect ~ level, data = theta_delay_midOccip_DFR_anova)
print("theta - delay midOccip")
## [1] "theta - delay midOccip"
print(summary(theta_delay_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 10.25 5.123 3.735 0.0262 *
## Residuals 148 203.01 1.372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
theta_probe_midOccip.aov <- aov(load_effect ~ level, data = theta_probe_midOccip_DFR_anova)
print("theta - probe midOccip")
## [1] "theta - probe midOccip"
print(summary(theta_probe_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 12.7 6.374 1.788 0.171
## Residuals 148 527.7 3.565
theta_cue_Oz.aov <- aov(load_effect ~ level, data = theta_cue_Oz_DFR_anova)
print("theta - cue Oz")
## [1] "theta - cue Oz"
print(summary(theta_cue_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 7.62 3.811 4.965 0.0081 **
## Residuals 158 121.27 0.768
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
theta_delay_Oz.aov <- aov(load_effect ~ level, data = theta_delay_Oz_DFR_anova)
print("theta - delay Oz")
## [1] "theta - delay Oz"
print(summary(theta_delay_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.87 0.434 0.371 0.69
## Residuals 158 184.61 1.168
theta_probe_Oz.aov <- aov(load_effect ~ level, data = theta_probe_Oz_DFR_anova)
print("theta - probe Oz")
## [1] "theta - probe Oz"
print(summary(theta_probe_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.4 0.1805 0.075 0.928
## Residuals 158 380.2 2.4064
theta_cue_Favg.aov <- aov(load_effect ~ level, data = theta_cue_Favg_DFR_anova)
print("theta - cue Favg")
## [1] "theta - cue Favg"
print(summary(theta_cue_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 84 42.02 2.587 0.0784 .
## Residuals 158 2567 16.24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
theta_delay_Favg.aov <- aov(load_effect ~ level, data = theta_delay_Favg_DFR_anova)
print("theta - delay Favg")
## [1] "theta - delay Favg"
print(summary(theta_delay_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 36 17.83 0.577 0.563
## Residuals 158 4882 30.90
theta_probe_Favg.aov <- aov(load_effect ~ level, data = theta_probe_Favg_DFR_anova)
print("theta - probe Favg")
## [1] "theta - probe Favg"
print(summary(theta_probe_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 114 56.81 0.314 0.731
## Residuals 158 28583 180.91
Significant load effects during delay and probe in the mid Occipital cluster and Oz electrode (low > high) and F avg during probe (low > high).
low_gamma_plot_list[["ERSPS_midOccip_DFR"]][["indiv_loads"]] + low_gamma_plot_list[["ERSPS_midOccip_DFR"]][["LE"]] +
plot_annotation(title="midOccip - low_gamma during DFR")
low_gamma_plot_list[["ERSPS_Oz_DFR"]][["indiv_loads"]] + low_gamma_plot_list[["ERSPS_Oz_DFR"]][["LE"]] +
plot_annotation(title="Oz - low_gamma during DFR")
low_gamma_plot_list[["ERSPS_Favg_DFR"]][["indiv_loads"]] + low_gamma_plot_list[["ERSPS_Favg_DFR"]][["LE"]] +
plot_annotation(title="F avg - low_gamma during DFR")
low_gamma_cue_average_midOccip <- select_period_average(low_gamma[["ERSPS_midOccip_DFR"]],0,2531.25,ERSPS_times_DFR)
low_gamma_cue_average_Oz <- select_period_average(low_gamma[["ERSPS_Oz_DFR"]],0,2531.25,ERSPS_times_DFR)
low_gamma_cue_average_Favg <- select_period_average(low_gamma[["ERSPS_Favg_DFR"]],0,2531.25,ERSPS_times_DFR)
low_gamma_delay_average_midOccip <- select_period_average(low_gamma[["ERSPS_midOccip_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
low_gamma_delay_average_Oz <- select_period_average(low_gamma[["ERSPS_Oz_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
low_gamma_delay_average_Favg <- select_period_average(low_gamma[["ERSPS_Favg_DFR"]],2531.25,5511.71875,ERSPS_times_DFR)
low_gamma_probe_average_midOccip <- select_period_average(low_gamma[["ERSPS_midOccip_DFR"]],5511.71875,7000,ERSPS_times_DFR)
low_gamma_probe_average_Oz <- select_period_average(low_gamma[["ERSPS_Oz_DFR"]],5511.71875,7000,ERSPS_times_DFR)
low_gamma_probe_average_Favg <- select_period_average(low_gamma[["ERSPS_Favg_DFR"]],5511.71875,7000,ERSPS_times_DFR)
t.test(low_gamma_cue_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: low_gamma_cue_average_midOccip$load_effect
## t = -0.14199, df = 177, p-value = 0.8872
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.06904871 0.05977946
## sample estimates:
## mean of x
## -0.004634626
t.test(low_gamma_cue_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: low_gamma_cue_average_Oz$load_effect
## t = -1.4566, df = 189, p-value = 0.1469
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.21352909 0.03212947
## sample estimates:
## mean of x
## -0.09069981
t.test(low_gamma_cue_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: low_gamma_cue_average_Favg$load_effect
## t = -0.70598, df = 189, p-value = 0.4811
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8515969 0.4026952
## sample estimates:
## mean of x
## -0.2244509
t.test(low_gamma_delay_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: low_gamma_delay_average_midOccip$load_effect
## t = -2.4175, df = 177, p-value = 0.01664
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.19328508 -0.01954772
## sample estimates:
## mean of x
## -0.1064164
t.test(low_gamma_delay_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: low_gamma_delay_average_Oz$load_effect
## t = -2.0996, df = 189, p-value = 0.03709
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.295546088 -0.009216663
## sample estimates:
## mean of x
## -0.1523814
t.test(low_gamma_delay_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: low_gamma_delay_average_Favg$load_effect
## t = -1.5405, df = 189, p-value = 0.1251
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.6600909 0.2041998
## sample estimates:
## mean of x
## -0.7279456
t.test(low_gamma_probe_average_midOccip$load_effect,mu=0)
##
## One Sample t-test
##
## data: low_gamma_probe_average_midOccip$load_effect
## t = -3.8094, df = 177, p-value = 0.0001919
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.6239510 -0.1980945
## sample estimates:
## mean of x
## -0.4110228
t.test(low_gamma_probe_average_Oz$load_effect,mu=0)
##
## One Sample t-test
##
## data: low_gamma_probe_average_Oz$load_effect
## t = -3.7986, df = 189, p-value = 0.000196
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.6970924 -0.2205602
## sample estimates:
## mean of x
## -0.4588263
t.test(low_gamma_probe_average_Favg$load_effect,mu=0)
##
## One Sample t-test
##
## data: low_gamma_probe_average_Favg$load_effect
## t = -3.4732, df = 189, p-value = 0.0006376
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -4.731170 -1.303682
## sample estimates:
## mean of x
## -3.017426
In the low gamma band, the only load effect that shows any difference is Oz during cue, with a trend for an inverted U shape relationship.
In the raw power, however, we do see differences between groups in the cue and delay periods in the mid occipcital cluster and Oz electrodes. In the cue and delay for mid occipital and delay for Oz, we see a significant low > high difference, and in cue period for the mid occipital and cue and delay for Oz, we see a med > high differnece.
midOccip_DFR_low_gamma <- paired_freq_plot(split_low_gammas_plot[["ERSPS_midOccip_DFR"]][["long"]], c("high","low", "load_effect"))
Oz_DFR_low_gamma <- paired_freq_plot(split_low_gammas_plot[["ERSPS_Oz_DFR"]][["long"]], c("high","low", "load_effect"))
Favg_DFR_low_gamma <- paired_freq_plot(split_low_gammas_plot[["ERSPS_Favg_DFR"]][["long"]], c("high","low", "load_effect"))
midOccip_DFR_low_gamma[[1]] + midOccip_DFR_low_gamma[[2]]+
plot_annotation(title="midOccip - low_gamma during DFR")+
plot_layout(guides="collect")
Oz_DFR_low_gamma[[1]] + Oz_DFR_low_gamma[[2]]+
plot_annotation(title="Oz - low_gamma during DFR")+
plot_layout(guides="collect")
Favg_DFR_low_gamma[[1]] + Favg_DFR_low_gamma[[2]]+
plot_annotation(title="F avg electrodes - low_gamma during DFR")+
plot_layout(guides="collect")
low_gamma_cue_Oz_DFR_split <- split_into_groups(low_gamma_cue_average_Oz, WM_groups)
low_gamma_cue_midOccip_DFR_split <- split_into_groups(low_gamma_cue_average_midOccip, WM_groups)
low_gamma_cue_Favg_DFR_split <- split_into_groups(low_gamma_cue_average_Favg, WM_groups)
low_gamma_delay_Oz_DFR_split <- split_into_groups(low_gamma_delay_average_Oz, WM_groups)
low_gamma_delay_midOccip_DFR_split <- split_into_groups(low_gamma_delay_average_midOccip, WM_groups)
low_gamma_delay_Favg_DFR_split <- split_into_groups(low_gamma_delay_average_Favg, WM_groups)
low_gamma_probe_Oz_DFR_split <- split_into_groups(low_gamma_probe_average_Oz, WM_groups)
low_gamma_probe_midOccip_DFR_split <- split_into_groups(low_gamma_probe_average_midOccip, WM_groups)
low_gamma_probe_Favg_DFR_split <- split_into_groups(low_gamma_probe_average_Favg, WM_groups)
low_gamma_cue_Oz_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_cue_Oz_DFR_split)
low_gamma_cue_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_cue_midOccip_DFR_split)
low_gamma_cue_Favg_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_cue_Favg_DFR_split)
low_gamma_delay_Oz_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_delay_Oz_DFR_split)
low_gamma_delay_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_delay_midOccip_DFR_split)
low_gamma_delay_Favg_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_delay_Favg_DFR_split)
low_gamma_probe_Oz_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_probe_Oz_DFR_split)
low_gamma_probe_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_probe_midOccip_DFR_split)
low_gamma_probe_Favg_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_probe_Favg_DFR_split)
low_gamma_cue_midOccip_DFR_anova <- merge(low_gamma_cue_average_midOccip,WM_to_merge, by="PTID")
low_gamma_delay_midOccip_DFR_anova <- merge(low_gamma_delay_average_midOccip,WM_to_merge, by="PTID")
low_gamma_probe_midOccip_DFR_anova <- merge(low_gamma_probe_average_midOccip,WM_to_merge, by="PTID")
low_gamma_cue_Oz_DFR_anova <- merge(low_gamma_cue_average_Oz,WM_to_merge, by="PTID")
low_gamma_delay_Oz_DFR_anova <- merge(low_gamma_delay_average_Oz,WM_to_merge, by="PTID")
low_gamma_probe_Oz_DFR_anova <- merge(low_gamma_probe_average_Oz,WM_to_merge, by="PTID")
low_gamma_cue_Favg_DFR_anova <- merge(low_gamma_cue_average_Favg,WM_to_merge, by="PTID")
low_gamma_delay_Favg_DFR_anova <- merge(low_gamma_delay_average_Favg,WM_to_merge, by="PTID")
low_gamma_probe_Favg_DFR_anova <- merge(low_gamma_probe_average_Favg,WM_to_merge, by="PTID")
low_gamma_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_cue_Oz_high
low_gamma_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_cue_midOccip_high
low_gamma_cue_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_cue_Favg_high
low_gamma_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_delay_Oz_high
low_gamma_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_delay_midOccip_high
low_gamma_delay_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_delay_Favg_high
low_gamma_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_probe_Oz_high
low_gamma_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_probe_midOccip_high
low_gamma_probe_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="high_load") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_probe_Favg_high
low_gamma_cue_Oz_high + low_gamma_delay_Oz_high + low_gamma_probe_Oz_high+
plot_annotation(title="Oz electrode low_gamma frequency in high load trials during DFR")
low_gamma_cue_Favg_high + low_gamma_delay_Favg_high + low_gamma_probe_Favg_high+
plot_annotation(title="F avg electrodes low_gamma frequency high load trials during DFR")
low_gamma_cue_midOccip_high + low_gamma_delay_midOccip_high + low_gamma_probe_midOccip_high+
plot_annotation(title="midOccip cluster low_gamma frequency high load trials during DFR")
low_gamma_cue_midOccip.aov <- aov(high_load ~ level, data = low_gamma_cue_midOccip_DFR_anova)
print("low_gamma - cue midOccip")
## [1] "low_gamma - cue midOccip"
print(summary(low_gamma_cue_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.833 0.9167 4.34 0.0147 *
## Residuals 148 31.257 0.2112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print(TukeyHSD(low_gamma_cue_midOccip.aov))
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = high_load ~ level, data = low_gamma_cue_midOccip_DFR_anova)
##
## $level
## diff lwr upr p adj
## low-high 0.23611702 0.01932486 0.4529092 0.0291757
## med-high 0.22549227 0.01098742 0.4399971 0.0368705
## med-low -0.01062475 -0.23048608 0.2092366 0.9928098
low_gamma_delay_midOccip.aov <- aov(high_load ~ level, data = low_gamma_delay_midOccip_DFR_anova)
print("low_gamma - delay midOccip")
## [1] "low_gamma - delay midOccip"
print(summary(low_gamma_delay_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.377 0.6885 5.005 0.00788 **
## Residuals 148 20.358 0.1376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print(TukeyHSD(low_gamma_delay_midOccip.aov))
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = high_load ~ level, data = low_gamma_delay_midOccip_DFR_anova)
##
## $level
## diff lwr upr p adj
## low-high 0.2332313 0.05827371 0.40818888 0.0054711
## med-high 0.1251381 -0.04797354 0.29824977 0.2043084
## med-low -0.1080932 -0.28552768 0.06934131 0.3219788
low_gamma_probe_midOccip.aov <- aov(high_load ~ level, data = low_gamma_probe_midOccip_DFR_anova)
print("low_gamma - probe midOccip")
## [1] "low_gamma - probe midOccip"
print(summary(low_gamma_probe_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.735 0.3675 2.052 0.132
## Residuals 148 26.500 0.1791
low_gamma_cue_Oz.aov <- aov(high_load ~ level, data = low_gamma_cue_Oz_DFR_anova)
print("low_gamma - cue Oz")
## [1] "low_gamma - cue Oz"
print(summary(low_gamma_cue_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 12.64 6.318 3.994 0.0203 *
## Residuals 158 249.95 1.582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print(TukeyHSD(low_gamma_cue_Oz.aov))
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = high_load ~ level, data = low_gamma_cue_Oz_DFR_anova)
##
## $level
## diff lwr upr p adj
## low-high 0.4878716 -0.08772857 1.0634718 0.1142282
## med-high 0.6552395 0.08514466 1.2253343 0.0197941
## med-low 0.1673678 -0.41081666 0.7455523 0.7726439
low_gamma_delay_Oz.aov <- aov(high_load ~ level, data = low_gamma_delay_Oz_DFR_anova)
print("low_gamma - delay Oz")
## [1] "low_gamma - delay Oz"
print(summary(low_gamma_delay_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 9.67 4.834 4.558 0.0119 *
## Residuals 158 167.55 1.060
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
print(TukeyHSD(low_gamma_delay_Oz.aov))
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = high_load ~ level, data = low_gamma_delay_Oz_DFR_anova)
##
## $level
## diff lwr upr p adj
## low-high 0.53339700 0.06213393 1.0046601 0.0221939
## med-high 0.49896157 0.03220594 0.9657172 0.0330488
## med-low -0.03443543 -0.50781435 0.4389435 0.9838034
low_gamma_probe_Oz.aov <- aov(high_load ~ level, data = low_gamma_probe_Oz_DFR_anova)
print("low_gamma - probe Oz")
## [1] "low_gamma - probe Oz"
print(summary(low_gamma_probe_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.88 0.4396 0.29 0.748
## Residuals 158 239.22 1.5140
low_gamma_cue_Favg.aov <- aov(high_load ~ level, data = low_gamma_cue_Favg_DFR_anova)
print("low_gamma - cue Favg")
## [1] "low_gamma - cue Favg"
print(summary(low_gamma_cue_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.63 1.816 1.517 0.223
## Residuals 158 189.17 1.197
low_gamma_delay_Favg.aov <- aov(high_load ~ level, data = low_gamma_delay_Favg_DFR_anova)
print("low_gamma - delay Favg")
## [1] "low_gamma - delay Favg"
print(summary(low_gamma_delay_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.84 1.921 1.82 0.165
## Residuals 158 166.76 1.056
low_gamma_probe_Favg.aov <- aov(high_load ~ level, data = low_gamma_probe_Favg_DFR_anova)
print("low_gamma - probe Favg")
## [1] "low_gamma - probe Favg"
print(summary(low_gamma_probe_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.19 0.5934 0.5 0.608
## Residuals 158 187.66 1.1877
low_gamma_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_cue_Oz_LE
low_gamma_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_cue_midOccip_LE
low_gamma_cue_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_cue_Favg_LE
low_gamma_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_delay_Oz_LE
low_gamma_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_delay_midOccip_LE
low_gamma_delay_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_delay_Favg_LE
low_gamma_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_probe_Oz_LE
low_gamma_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_probe_midOccip_LE
low_gamma_probe_Favg_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_probe_Favg_LE
low_gamma_cue_Oz_LE + low_gamma_delay_Oz_LE + low_gamma_probe_Oz_LE+
plot_annotation(title="Oz electrode low_gamma frequency Load Effect during DFR")
low_gamma_cue_Favg_LE + low_gamma_delay_Favg_LE + low_gamma_probe_Favg_LE+
plot_annotation(title="F avg electrodes low_gamma frequency Load Effect during DFR")
low_gamma_cue_midOccip_LE + low_gamma_delay_midOccip_LE + low_gamma_probe_midOccip_LE+
plot_annotation(title="midOccip cluster low_gamma frequency Load Effect during DFR")
low_gamma_cue_midOccip.aov <- aov(load_effect ~ level, data = low_gamma_cue_midOccip_DFR_anova)
print("low_gamma - cue midOccip")
## [1] "low_gamma - cue midOccip"
print(summary(low_gamma_cue_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.075 0.03768 0.222 0.801
## Residuals 148 25.132 0.16981
low_gamma_delay_midOccip.aov <- aov(load_effect ~ level, data = low_gamma_delay_midOccip_DFR_anova)
print("low_gamma - delay midOccip")
## [1] "low_gamma - delay midOccip"
print(summary(low_gamma_delay_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.37 0.1856 0.577 0.563
## Residuals 148 47.57 0.3214
low_gamma_probe_midOccip.aov <- aov(load_effect ~ level, data = low_gamma_probe_midOccip_DFR_anova)
print("low_gamma - probe midOccip")
## [1] "low_gamma - probe midOccip"
print(summary(low_gamma_probe_midOccip.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 4.5 2.255 0.964 0.384
## Residuals 148 346.3 2.340
low_gamma_cue_Oz.aov <- aov(load_effect ~ level, data = low_gamma_cue_Oz_DFR_anova)
print("low_gamma - cue Oz")
## [1] "low_gamma - cue Oz"
print(summary(low_gamma_cue_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.58 1.7882 2.724 0.0687 .
## Residuals 158 103.73 0.6565
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
low_gamma_delay_Oz.aov <- aov(load_effect ~ level, data = low_gamma_delay_Oz_DFR_anova)
print("low_gamma - delay Oz")
## [1] "low_gamma - delay Oz"
print(summary(low_gamma_delay_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.64 0.3200 0.386 0.68
## Residuals 158 130.95 0.8288
low_gamma_probe_Oz.aov <- aov(load_effect ~ level, data = low_gamma_probe_Oz_DFR_anova)
print("low_gamma - probe Oz")
## [1] "low_gamma - probe Oz"
print(summary(low_gamma_probe_Oz.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.9 0.9627 0.335 0.716
## Residuals 158 454.3 2.8753
low_gamma_cue_Favg.aov <- aov(load_effect ~ level, data = low_gamma_cue_Favg_DFR_anova)
print("low_gamma - cue Favg")
## [1] "low_gamma - cue Favg"
print(summary(low_gamma_cue_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 63.1 31.57 2.026 0.135
## Residuals 158 2462.7 15.59
low_gamma_delay_Favg.aov <- aov(load_effect ~ level, data = low_gamma_delay_Favg_DFR_anova)
print("low_gamma - delay Favg")
## [1] "low_gamma - delay Favg"
print(summary(low_gamma_delay_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 81 40.74 1.167 0.314
## Residuals 158 5515 34.90
low_gamma_probe_Favg.aov <- aov(load_effect ~ level, data = low_gamma_probe_Favg_DFR_anova)
print("low_gamma - probe Favg")
## [1] "low_gamma - probe Favg"
print(summary(low_gamma_probe_Favg.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 250 124.8 0.811 0.446
## Residuals 158 24298 153.8
The only significant PAC measure across WMC groups is the theta - beta coupling in the Fz electrode, with an inverted U shaped relationship.
PAC_midOccip <- list(high_load=read_delim("data/EEG/DFR/PAC/PAC_cl15_midOccip_maint_design1_highload.txt", delim=","), low_load = read_delim("data/EEG/DFR/PAC/PAC_cl15_midOccip_maint_design1_lowload.txt", delim=",") )
PAC_midFront <- list(high_load=read_delim("data/EEG/DFR/PAC/PAC_cl5_midFront_maint_design1_highload.txt", delim=","), low_load = read_delim("data/EEG/DFR/PAC/PAC_cl5_midFront_maint_design1_lowload.txt", delim=",") )
PAC_Oz <- list(high_load=read_delim("data/EEG/DFR/PAC/PAC_Oz_maint_design1_highload.txt", delim=","), low_load = read_delim("data/EEG/DFR/PAC/PAC_Oz_maint_design1_lowload.txt", delim=",") )
PAC_O1 <- list(high_load=read_delim("data/EEG/DFR/PAC/PAC_O1_maint_design1_highload.txt", delim=","), low_load = read_delim("data/EEG/DFR/PAC/PAC_O1_maint_design1_lowload.txt", delim=",") )
PAC_O2 <- list(high_load=read_delim("data/EEG/DFR/PAC/PAC_O2_maint_design1_highload.txt", delim=","), low_load = read_delim("data/EEG/DFR/PAC/PAC_O2_maint_design1_lowload.txt", delim=",") )
PAC_Fz <- list(high_load=read_delim("data/EEG/DFR/PAC/PAC_Fz_maint_design1_highload.txt", delim=","), low_load = read_delim("data/EEG/DFR/PAC/PAC_Fz_maint_design1_lowload.txt", delim=",") )
PAC_list <- list(midOccip = PAC_midOccip, midFront = PAC_midFront, Oz = PAC_Oz, O1 = PAC_O1, O2 = PAC_O2, Fz = PAC_Fz)
for (PAC in seq.int(1,length(PAC_list))){
for (level in seq.int(1,2)){
colnames(PAC_list[[PAC]][[level]]) <- c("PTID", "thetP_hbetA_mnt", "thetP_lgamA_mnt", "thetP_hgamA_mnt", "thetP_ugamA_mnt", "alphP_hbetA_mnt", "alphP_lgamA_mnt", "alphP_hgamA_mnt", "alphP_ugamA_mnt")
PAC_list[[PAC]][[level]] <- PAC_list[[PAC]][[level]][,1:9]
}
PAC_list[[PAC]][["load_effect"]] <- PAC_list[[PAC]][["high_load"]] - PAC_list[[PAC]][["low_load"]]
PAC_list[[PAC]][["load_effect"]]$PTID <- PAC_list[[PAC]][["high_load"]]$PTID
PAC_list[[PAC]][["load_effect"]] <- merge(PAC_list[[PAC]][["load_effect"]], constructs_fMRI, by = "PTID")
PAC_list[[PAC]][["load_effect"]] <- merge(PAC_list[[PAC]][["load_effect"]], WM_to_merge, by = "PTID")
PAC_list[[PAC]][["high_load"]] <- merge(PAC_list[[PAC]][["high_load"]], constructs_fMRI, by = "PTID")
PAC_list[[PAC]][["high_load"]] <- merge(PAC_list[[PAC]][["high_load"]], WM_to_merge, by = "PTID")
}
for (PAC in seq.int(1,length(PAC_list))){
print(ggplot(data = PAC_list[[PAC]][["high_load"]], aes(x = omnibus_span_no_DFR, y = thetP_hbetA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
print(ggplot(data = PAC_list[[PAC]][["high_load"]], aes(x = omnibus_span_no_DFR, y = thetP_lgamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
print(ggplot(data = PAC_list[[PAC]][["high_load"]], aes(x = omnibus_span_no_DFR, y = thetP_hgamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
print(ggplot(data = PAC_list[[PAC]][["high_load"]], aes(x = omnibus_span_no_DFR, y = thetP_ugamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
}
for (PAC in seq.int(1,length(PAC_list))){
print(ggplot(data = PAC_list[[PAC]][["load_effect"]], aes(x = omnibus_span_no_DFR, y = thetP_hbetA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
print(ggplot(data = PAC_list[[PAC]][["load_effect"]], aes(x = omnibus_span_no_DFR, y = thetP_lgamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
print(ggplot(data = PAC_list[[PAC]][["load_effect"]], aes(x = omnibus_span_no_DFR, y = thetP_hgamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
print(ggplot(data = PAC_list[[PAC]][["load_effect"]], aes(x = omnibus_span_no_DFR, y = thetP_ugamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
}
for (PAC in seq.int(1,length(PAC_list))){
print(ggplot(data = PAC_list[[PAC]][["high_load"]], aes(x = omnibus_span_no_DFR, y = alphP_lgamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
print(ggplot(data = PAC_list[[PAC]][["high_load"]], aes(x = omnibus_span_no_DFR, y = alphP_hgamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
print(ggplot(data = PAC_list[[PAC]][["high_load"]], aes(x = omnibus_span_no_DFR, y = alphP_ugamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
}
## Warning: Removed 17 rows containing non-finite values (stat_smooth).
## Warning: Removed 17 rows containing missing values (geom_point).
## Warning: Removed 17 rows containing non-finite values (stat_smooth).
## Warning: Removed 17 rows containing missing values (geom_point).
## Warning: Removed 17 rows containing non-finite values (stat_smooth).
## Warning: Removed 17 rows containing missing values (geom_point).
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
for (PAC in seq.int(1,length(PAC_list))){
print(ggplot(data = PAC_list[[PAC]][["load_effect"]], aes(x = omnibus_span_no_DFR, y = alphP_lgamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
print(ggplot(data = PAC_list[[PAC]][["load_effect"]], aes(x = omnibus_span_no_DFR, y = alphP_hgamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
print(ggplot(data = PAC_list[[PAC]][["load_effect"]], aes(x = omnibus_span_no_DFR, y = alphP_ugamA_mnt))+
geom_point()+
stat_smooth(method="lm")+
ggtitle(names(PAC_list)[PAC]))
}
## Warning: Removed 17 rows containing non-finite values (stat_smooth).
## Warning: Removed 17 rows containing missing values (geom_point).
## Warning: Removed 17 rows containing non-finite values (stat_smooth).
## Warning: Removed 17 rows containing missing values (geom_point).
## Warning: Removed 17 rows containing non-finite values (stat_smooth).
## Warning: Removed 17 rows containing missing values (geom_point).
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
## Warning: Removed 27 rows containing non-finite values (stat_smooth).
## Warning: Removed 27 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
## Warning: Removed 7 rows containing non-finite values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).
split_PAC <- list()
split_PAC_prepped <- list()
split_PAC_high <- list()
split_PAC_high_prepped <- list()
for (PAC in seq.int(1,length(PAC_list))){
split_PAC[[PAC]] <- split_into_groups(PAC_list[[PAC]][["load_effect"]], WM_groups)
split_PAC_prepped[[PAC]] <- prep_split_for_bar_plots(split_PAC[[PAC]])
split_PAC_high[[PAC]] <- split_into_groups(PAC_list[[PAC]][["high_load"]], WM_groups)
split_PAC_high_prepped[[PAC]] <- prep_split_for_bar_plots(split_PAC_high[[PAC]])
}
names(split_PAC) <- names(PAC_list)
names(split_PAC_prepped) <- names(PAC_list)
names(split_PAC_high) <- names(PAC_list)
names(split_PAC_high_prepped) <- names(PAC_list)
PAC_bar_plots_high <- list(list(), list(),list(), list(),list(), list())
for (PAC in seq.int(1,length(PAC_list))){
split_PAC_high_prepped[[PAC]][["melt_data"]] %>%
filter(variable == "thetP_hbetA_mnt") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
#ggtitle(names(PAC_list)[PAC])+
xlab("WMC")+
ylab("thetP_betA_mnt")+
theme_classic()+
theme(aspect.ratio = 1) -> PAC_bar_plots_high[[PAC]][["thetP_betA_mnt"]]
split_PAC_high_prepped[[PAC]][["melt_data"]] %>%
filter(variable == "thetP_lgamA_mnt") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
#ggtitle(names(PAC_list)[PAC])+
xlab("WMC")+
ylab("thetP_lgamA_mnt")+
theme_classic()+
theme(aspect.ratio = 1) -> PAC_bar_plots_high[[PAC]][["thetP_lgamA_mnt"]]
split_PAC_high_prepped[[PAC]][["melt_data"]] %>%
filter(variable == "thetP_hgamA_mnt") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
#ggtitle(names(PAC_list)[PAC])+
xlab("WMC")+
ylab("thetP_hgamA_mnt")+
theme_classic()+
theme(aspect.ratio = 1) -> PAC_bar_plots_high[[PAC]][["thetP_hgamA_mnt"]]
split_PAC_high_prepped[[PAC]][["melt_data"]] %>%
filter(variable == "thetP_ugamA_mnt") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
#ggtitle(names(PAC_list)[PAC])+
xlab("WMC")+
ylab("thetP_ugamA_mnt")+
theme_classic()+
theme(aspect.ratio = 1) -> PAC_bar_plots_high[[PAC]][["thetP_ugamA_mnt"]]
}
names(PAC_bar_plots_high) <- names(PAC_list)
PAC_bar_plots <- list(list(), list(),list(), list(),list(), list())
for (PAC in seq.int(1,length(PAC_list))){
split_PAC_prepped[[PAC]][["melt_data"]] %>%
filter(variable == "thetP_hbetA_mnt") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
#ggtitle(names(PAC_list)[PAC])+
xlab("WMC")+
ylab("thetP_betA_mnt")+
theme_classic()+
theme(aspect.ratio = 1) -> PAC_bar_plots[[PAC]][["thetP_betA_mnt"]]
split_PAC_prepped[[PAC]][["melt_data"]] %>%
filter(variable == "thetP_lgamA_mnt") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
#ggtitle(names(PAC_list)[PAC])+
xlab("WMC")+
ylab("thetP_lgamA_mnt")+
theme_classic()+
theme(aspect.ratio = 1) -> PAC_bar_plots[[PAC]][["thetP_lgamA_mnt"]]
split_PAC_prepped[[PAC]][["melt_data"]] %>%
filter(variable == "thetP_hgamA_mnt") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
#ggtitle(names(PAC_list)[PAC])+
xlab("WMC")+
ylab("thetP_hgamA_mnt")+
theme_classic()+
theme(aspect.ratio = 1) -> PAC_bar_plots[[PAC]][["thetP_hgamA_mnt"]]
split_PAC_prepped[[PAC]][["melt_data"]] %>%
filter(variable == "thetP_ugamA_mnt") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
#ggtitle(names(PAC_list)[PAC])+
xlab("WMC")+
ylab("thetP_ugamA_mnt")+
theme_classic()+
theme(aspect.ratio = 1) -> PAC_bar_plots[[PAC]][["thetP_ugamA_mnt"]]
}
names(PAC_bar_plots) <- names(PAC_list)
for (PAC in seq.int(1,length(PAC_bar_plots_high))){
print(
(PAC_bar_plots_high[[PAC]][[1]] + PAC_bar_plots_high[[PAC]][[2]]) /
(PAC_bar_plots_high[[PAC]][[3]] + PAC_bar_plots_high[[PAC]][[4]]) +
plot_annotation(title = names(PAC_bar_plots_high)[PAC])
)
}
for (PAC in seq.int(1,length(PAC_list))){
for (measure in seq.int(2,5)){
print(paste0("Region: ", names(PAC_list)[PAC], ", measure: ",colnames(PAC_list[[PAC]][["high_load"]])[measure]))
temp.aov <- aov(PAC_list[[PAC]][["high_load"]][,measure] ~ PAC_list[[PAC]][["high_load"]]$level)
print(summary(temp.aov))
}
}
## [1] "Region: midOccip, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 2.35 1.174 0.717 0.49
## Residuals 148 242.17 1.636
## 17 observations deleted due to missingness
## [1] "Region: midOccip, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 2.17 1.087 0.878 0.418
## Residuals 148 183.38 1.239
## 17 observations deleted due to missingness
## [1] "Region: midOccip, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 0.62 0.3087 1.298 0.276
## Residuals 148 35.21 0.2379
## 17 observations deleted due to missingness
## [1] "Region: midOccip, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 0.55 0.2748 1.317 0.271
## Residuals 148 30.89 0.2087
## 17 observations deleted due to missingness
## [1] "Region: midFront, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 0.81 0.4053 1.125 0.328
## Residuals 138 49.72 0.3603
## 27 observations deleted due to missingness
## [1] "Region: midFront, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 0.15 0.07282 0.245 0.783
## Residuals 138 40.93 0.29660
## 27 observations deleted due to missingness
## [1] "Region: midFront, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 0.08 0.04154 0.163 0.85
## Residuals 138 35.13 0.25455
## 27 observations deleted due to missingness
## [1] "Region: midFront, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 0.19 0.09441 0.385 0.681
## Residuals 138 33.86 0.24539
## 27 observations deleted due to missingness
## [1] "Region: Oz, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 12.6 6.322 1.862 0.159
## Residuals 158 536.6 3.396
## 7 observations deleted due to missingness
## [1] "Region: Oz, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 0.19 0.0946 0.074 0.929
## Residuals 158 202.42 1.2812
## 7 observations deleted due to missingness
## [1] "Region: Oz, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 0.09 0.0442 0.044 0.957
## Residuals 158 159.59 1.0101
## 7 observations deleted due to missingness
## [1] "Region: Oz, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 1.71 0.8554 0.573 0.565
## Residuals 158 235.89 1.4930
## 7 observations deleted due to missingness
## [1] "Region: O1, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 0.3 0.1618 0.06 0.942
## Residuals 158 427.1 2.7033
## 7 observations deleted due to missingness
## [1] "Region: O1, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 1.8 0.9228 0.414 0.662
## Residuals 158 352.1 2.2286
## 7 observations deleted due to missingness
## [1] "Region: O1, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 0.37 0.187 0.184 0.832
## Residuals 158 160.90 1.018
## 7 observations deleted due to missingness
## [1] "Region: O1, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 3.36 1.678 1.384 0.254
## Residuals 158 191.51 1.212
## 7 observations deleted due to missingness
## [1] "Region: O2, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 29.5 14.752 5.016 0.00772 **
## Residuals 158 464.7 2.941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 7 observations deleted due to missingness
## [1] "Region: O2, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 1.2 0.6008 0.49 0.614
## Residuals 158 193.9 1.2270
## 7 observations deleted due to missingness
## [1] "Region: O2, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 1.0 0.5025 0.382 0.683
## Residuals 158 208.1 1.3170
## 7 observations deleted due to missingness
## [1] "Region: O2, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 8.83 4.417 2.208 0.113
## Residuals 158 315.99 2.000
## 7 observations deleted due to missingness
## [1] "Region: Fz, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 3.43 1.7170 2.916 0.0571 .
## Residuals 158 93.03 0.5888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 7 observations deleted due to missingness
## [1] "Region: Fz, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 1.38 0.6886 0.927 0.398
## Residuals 158 117.40 0.7430
## 7 observations deleted due to missingness
## [1] "Region: Fz, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 1.92 0.9615 2.959 0.0548 .
## Residuals 158 51.34 0.3250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 7 observations deleted due to missingness
## [1] "Region: Fz, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["high_load"]]$level 2 0.53 0.2651 0.707 0.495
## Residuals 158 59.27 0.3751
## 7 observations deleted due to missingness
for (PAC in seq.int(1,length(PAC_bar_plots))){
print(
(PAC_bar_plots[[PAC]][[1]] + PAC_bar_plots[[PAC]][[2]]) /
(PAC_bar_plots[[PAC]][[3]] + PAC_bar_plots[[PAC]][[4]]) +
plot_annotation(title = names(PAC_bar_plots)[PAC])
)
}
for (PAC in seq.int(1,length(PAC_list))){
for (measure in seq.int(2,5)){
print(paste0("Region: ", names(PAC_list)[PAC], ", measure: ",colnames(PAC_list[[PAC]][["load_effect"]])[measure]))
temp.aov <- aov(PAC_list[[PAC]][["load_effect"]][,measure] ~ PAC_list[[PAC]][["load_effect"]]$level)
print(summary(temp.aov))
}
}
## [1] "Region: midOccip, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 5.46 2.731 1.952 0.146
## Residuals 148 207.02 1.399
## 17 observations deleted due to missingness
## [1] "Region: midOccip, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 3.44 1.721 1.52 0.222
## Residuals 148 167.57 1.132
## 17 observations deleted due to missingness
## [1] "Region: midOccip, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 0.90 0.452 0.855 0.428
## Residuals 148 78.29 0.529
## 17 observations deleted due to missingness
## [1] "Region: midOccip, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 0.06 0.0297 0.061 0.941
## Residuals 148 71.94 0.4861
## 17 observations deleted due to missingness
## [1] "Region: midFront, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 1.22 0.6088 1.076 0.344
## Residuals 138 78.05 0.5656
## 27 observations deleted due to missingness
## [1] "Region: midFront, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 1.05 0.5239 1.001 0.37
## Residuals 138 72.25 0.5235
## 27 observations deleted due to missingness
## [1] "Region: midFront, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 0.18 0.0890 0.194 0.824
## Residuals 138 63.41 0.4595
## 27 observations deleted due to missingness
## [1] "Region: midFront, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 0.12 0.0586 0.119 0.888
## Residuals 138 67.80 0.4913
## 27 observations deleted due to missingness
## [1] "Region: Oz, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 9.0 4.490 1.349 0.263
## Residuals 158 525.9 3.329
## 7 observations deleted due to missingness
## [1] "Region: Oz, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 2.84 1.421 0.753 0.473
## Residuals 158 298.31 1.888
## 7 observations deleted due to missingness
## [1] "Region: Oz, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 4.36 2.178 1.159 0.316
## Residuals 158 296.85 1.879
## 7 observations deleted due to missingness
## [1] "Region: Oz, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 6.23 3.115 1.748 0.177
## Residuals 158 281.57 1.782
## 7 observations deleted due to missingness
## [1] "Region: O1, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 4.1 2.064 0.838 0.435
## Residuals 158 389.2 2.463
## 7 observations deleted due to missingness
## [1] "Region: O1, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 0.4 0.2044 0.087 0.917
## Residuals 158 371.2 2.3495
## 7 observations deleted due to missingness
## [1] "Region: O1, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 0.12 0.0622 0.045 0.956
## Residuals 158 219.13 1.3869
## 7 observations deleted due to missingness
## [1] "Region: O1, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 0.8 0.397 0.178 0.837
## Residuals 158 353.1 2.235
## 7 observations deleted due to missingness
## [1] "Region: O2, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 12.2 6.103 2.396 0.0944 .
## Residuals 158 402.5 2.548
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 7 observations deleted due to missingness
## [1] "Region: O2, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 4.4 2.206 0.956 0.387
## Residuals 158 364.5 2.307
## 7 observations deleted due to missingness
## [1] "Region: O2, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 2.1 1.071 0.358 0.7
## Residuals 158 473.1 2.994
## 7 observations deleted due to missingness
## [1] "Region: O2, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 13.1 6.572 2.505 0.0849 .
## Residuals 158 414.4 2.623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 7 observations deleted due to missingness
## [1] "Region: Fz, measure: thetP_hbetA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 9.58 4.790 4.522 0.0123 *
## Residuals 158 167.36 1.059
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 7 observations deleted due to missingness
## [1] "Region: Fz, measure: thetP_lgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 2.03 1.016 0.987 0.375
## Residuals 158 162.73 1.030
## 7 observations deleted due to missingness
## [1] "Region: Fz, measure: thetP_hgamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 0.47 0.2359 0.373 0.69
## Residuals 158 100.03 0.6331
## 7 observations deleted due to missingness
## [1] "Region: Fz, measure: thetP_ugamA_mnt"
## Df Sum Sq Mean Sq F value Pr(>F)
## PAC_list[[PAC]][["load_effect"]]$level 2 1.04 0.5225 0.715 0.491
## Residuals 158 115.46 0.7308
## 7 observations deleted due to missingness
CDA <- data.frame(read.csv("data/EEG/LCD/ERPS_CDA.txt"))
CDA_fMRI <- CDA[CDA$subID %in% constructs_fMRI$PTID,]
CDA_fMRI[is.nan(CDA_fMRI[,2]),2:11] <- NA
colnames(CDA_fMRI)[1] <- "PTID"
CDA_fMRI$CDA_3_1 <- CDA_fMRI$LCD_CDA_3dots - CDA_fMRI$LCD_CDA_1dots
CDA_fMRI$CDA_5_1 <- CDA_fMRI$LCD_CDA_5dots - CDA_fMRI$LCD_CDA_1dots
CDA_fMRI$CDA_5_3 <- CDA_fMRI$LCD_CDA_5dots - CDA_fMRI$LCD_CDA_3dots
First, let’s take a look at the raw data, just for sanity’s sake.
CDA_fMRI %>%
select(c("LCD_CDA_1dots", "LCD_CDA_3dots", "LCD_CDA_5dots")) %>%
melt() %>%
ggplot(aes(x=variable,y=value))+
geom_jitter()+
stat_summary(fun.data=mean_sdl, geom="pointrange", color="red")+
xlab("Level")+
ylab("CDA")
## No id variables; using all as measure variables
## Warning: Removed 24 rows containing non-finite values (stat_summary).
## Warning: Removed 24 rows containing missing values (geom_point).
CDA_fMRI %>%
select(c("CDA_3_1", "CDA_5_1", "CDA_5_3")) %>%
melt() %>%
ggplot(aes(x=variable,y=value))+
geom_jitter()+
stat_summary(fun.data=mean_sdl, geom="pointrange", color="red")+
xlab("Level differences")+
ylab("CDA Load Effects")
## No id variables; using all as measure variables
## Warning: Removed 24 rows containing non-finite values (stat_summary).
## Warning: Removed 24 rows containing missing values (geom_point).
Other studies have shown a relationship between the CDA and capacity as measured by LCD K. Our data, only showed a correlation between the CDA in the low load condition (1 dot) and the LCD K max, with no correlation between omnibus span or DFR load effect.
data_to_plot <- data.frame(CDA_fMRI, p200_delay_DFR, omnibus_span = constructs_fMRI$omnibus_span_no_DFR)
data_to_plot <- merge(data_to_plot,p200_cog)
(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=LCD_CDA_5dots))+
geom_point()+
stat_smooth(method="loess")+
theme_classic())+(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=LCD_CDA_3dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=LCD_CDA_1dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
cor.test(data_to_plot$LCD_CDA_5dots, data_to_plot$DFR_Load3_Load1)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_5dots and data_to_plot$DFR_Load3_Load1
## t = 0.1352, df = 160, p-value = 0.8926
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1437443 0.1646122
## sample estimates:
## cor
## 0.01068802
cor.test(data_to_plot$LCD_CDA_3dots, data_to_plot$DFR_Load3_Load1)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_3dots and data_to_plot$DFR_Load3_Load1
## t = -1.524, df = 160, p-value = 0.1295
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.26885513 0.03522659
## sample estimates:
## cor
## -0.1196186
cor.test(data_to_plot$LCD_CDA_1dots, data_to_plot$DFR_Load3_Load1)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_1dots and data_to_plot$DFR_Load3_Load1
## t = 0.92497, df = 160, p-value = 0.3564
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08218942 0.22460001
## sample estimates:
## cor
## 0.0729303
(ggplot(data_to_plot,aes(x=omnibus_span, y=LCD_CDA_5dots))+
geom_point()+
stat_smooth(method="loess")+
theme_classic())+(ggplot(data_to_plot,aes(x=omnibus_span, y=LCD_CDA_3dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=omnibus_span, y=LCD_CDA_1dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
cor.test(data_to_plot$LCD_CDA_5dots, data_to_plot$omnibus_span)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_5dots and data_to_plot$omnibus_span
## t = -0.81334, df = 160, p-value = 0.4172
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.21622386 0.09092732
## sample estimates:
## cor
## -0.06416781
cor.test(data_to_plot$LCD_CDA_3dots, data_to_plot$omnibus_span)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_3dots and data_to_plot$omnibus_span
## t = -1.4802, df = 160, p-value = 0.1408
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.26566128 0.03866141
## sample estimates:
## cor
## -0.1162269
cor.test(data_to_plot$LCD_CDA_1dots, data_to_plot$omnibus_span)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_1dots and data_to_plot$omnibus_span
## t = -1.3219, df = 160, p-value = 0.1881
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.25406112 0.05107662
## sample estimates:
## cor
## -0.1039374
(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=LCD_CDA_5dots))+
geom_point()+
stat_smooth(method="loess")+
theme_classic())+(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=LCD_CDA_3dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=LCD_CDA_1dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
cor.test(data_to_plot$LCD_CDA_5dots, data_to_plot$XLCD_K_MAX)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_5dots and data_to_plot$XLCD_K_MAX
## t = -0.48689, df = 159, p-value = 0.627
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1921126 0.1167875
## sample estimates:
## cor
## -0.03858431
cor.test(data_to_plot$LCD_CDA_3dots, data_to_plot$XLCD_K_MAX)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_3dots and data_to_plot$XLCD_K_MAX
## t = -1.2834, df = 159, p-value = 0.2012
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.25198309 0.05427012
## sample estimates:
## cor
## -0.1012547
cor.test(data_to_plot$LCD_CDA_1dots, data_to_plot$XLCD_K_MAX)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_1dots and data_to_plot$XLCD_K_MAX
## t = -2.0777, df = 159, p-value = 0.03935
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.309470392 -0.008106698
## sample estimates:
## cor
## -0.1625777
CDA_split <- split_into_groups(CDA_fMRI, WM_groups)
CDA_split_plot_data <- prep_split_for_bar_plots(CDA_split)
CDA_split_plot_data[["melt_data"]] %>%
filter(variable == "LCD_CDA_5dots") %>%
mutate(level = factor(level,levels=c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE,ymax=Means+SE), width=0.2)+
xlab("WMC group")+
ylab("CDA")+
ggtitle("CDA - 5 dots")+
theme_classic()+
theme(aspect.ratio=1)
CDA_split_plot_data[["melt_data"]] %>%
filter(variable == "LCD_CDA_1dots") %>%
mutate(level = factor(level,levels=c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE,ymax=Means+SE), width=0.2)+
xlab("WMC group")+
ylab("CDA")+
ggtitle("CDA - 1 dot")+
theme_classic()+
theme(aspect.ratio=1)
CDA_anova <- merge(CDA_fMRI,WM_to_merge, by="PTID")
summary(aov(LCD_CDA_1dots ~ level,data=CDA_anova))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.25 0.1266 0.43 0.651
## Residuals 157 46.25 0.2946
## 8 observations deleted due to missingness
Vogel and Machizawa (2004) had mean K = 2.8. Our average K max = 2.68 (so slightly lower). The only thing that even shows a trend with DFR fMRI load effects is in the load effect between the 3 dot and 1 dot condition.
(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=CDA_5_3))+
geom_point()+
stat_smooth(method="loess")+
theme_classic())+(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=CDA_5_1))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=CDA_3_1))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
cor.test(data_to_plot$CDA_5_3, data_to_plot$DFR_Load3_Load1)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_3 and data_to_plot$DFR_Load3_Load1
## t = 1.3562, df = 160, p-value = 0.177
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04838862 0.25658013
## sample estimates:
## cor
## 0.1066022
cor.test(data_to_plot$CDA_5_1, data_to_plot$DFR_Load3_Load1)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_1 and data_to_plot$DFR_Load3_Load1
## t = -0.56408, df = 160, p-value = 0.5735
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1973896 0.1104038
## sample estimates:
## cor
## -0.04455009
cor.test(data_to_plot$CDA_3_1, data_to_plot$DFR_Load3_Load1)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_3_1 and data_to_plot$DFR_Load3_Load1
## t = -1.9681, df = 160, p-value = 0.05079
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3008041900 0.0004669451
## sample estimates:
## cor
## -0.1537396
(ggplot(data_to_plot,aes(x=omnibus_span, y=CDA_5_3))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=omnibus_span, y=CDA_5_1))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=omnibus_span, y=CDA_3_1))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)
)
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
cor.test(data_to_plot$CDA_5_3, data_to_plot$omnibus_span)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_3 and data_to_plot$omnibus_span
## t = 0.59677, df = 160, p-value = 0.5515
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1078529 0.1998694
## sample estimates:
## cor
## 0.04712628
cor.test(data_to_plot$CDA_5_1, data_to_plot$omnibus_span)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_1 and data_to_plot$omnibus_span
## t = 0.30731, df = 160, p-value = 0.759
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1303964 0.1778170
## sample estimates:
## cor
## 0.02428743
cor.test(data_to_plot$CDA_3_1, data_to_plot$omnibus_span)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_3_1 and data_to_plot$omnibus_span
## t = -0.31616, df = 160, p-value = 0.7523
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1784947 0.1297082
## sample estimates:
## cor
## -0.024987
mean(data_to_plot$XLCD_K_MAX,na.rm=TRUE)
## [1] 2.686799
(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=CDA_5_3))+
geom_point()+
stat_smooth(method="loess")+
theme_classic()+
theme(aspect.ratio=1))+(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=CDA_5_1))+
geom_point()+
stat_smooth(method="loess")+
theme_classic())+(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=CDA_3_1))+
geom_point()+
stat_smooth(method="loess")+
theme_classic())
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
cor.test(data_to_plot$CDA_5_3, data_to_plot$XLCD_K_MAX)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_3 and data_to_plot$XLCD_K_MAX
## t = 0.68698, df = 159, p-value = 0.4931
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1011252 0.2073309
## sample estimates:
## cor
## 0.05440059
cor.test(data_to_plot$CDA_5_1, data_to_plot$XLCD_K_MAX)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_1 and data_to_plot$XLCD_K_MAX
## t = 1.118, df = 159, p-value = 0.2652
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06727587 0.23971818
## sample estimates:
## cor
## 0.08831801
cor.test(data_to_plot$CDA_3_1, data_to_plot$XLCD_K_MAX)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_3_1 and data_to_plot$XLCD_K_MAX
## t = 0.37401, df = 159, p-value = 0.7089
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1256028 0.1834815
## sample estimates:
## cor
## 0.02964807
ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=omnibus_span))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).
cor.test(data_to_plot$omnibus_span, data_to_plot$XLCD_K_MAX)
##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span and data_to_plot$XLCD_K_MAX
## t = 8.8404, df = 165, p-value = 1.384e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4541707 0.6618156
## sample estimates:
## cor
## 0.5669319
Numerically, it looks like there is an inverted U shaped relationship for the 5 dots - 1 dot and 3 dot - 1 dot, but those relationships are not significant.
CDA_split_plot_data[["melt_data"]] %>%
filter(variable == "CDA_5_3") %>%
mutate(level = factor(level,levels=c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE,ymax=Means+SE), width=0.2)+
xlab("WMC group")+
ylab("CDA Load Effect")+
ggtitle("5 dots - 3 dots")+
theme_classic()+
theme(aspect.ratio=1)
CDA_split_plot_data[["melt_data"]] %>%
filter(variable == "CDA_5_1") %>%
mutate(level = factor(level,levels=c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE,ymax=Means+SE), width=0.2)+
xlab("WMC group")+
ylab("CDA Load Effect")+
ggtitle("5 dots - 1 dot")+
theme_classic()+
theme(aspect.ratio=1)
CDA_split_plot_data[["melt_data"]] %>%
filter(variable == "CDA_3_1") %>%
mutate(level = factor(level,levels=c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE,ymax=Means+SE), width=0.2)+
xlab("WMC group")+
ylab("CDA Load Effect")+
ggtitle("3 dots - 1 dot")+
theme_classic()+
theme(aspect.ratio=1)
CDA_5_3.aov <- aov(CDA_5_3 ~ level, data = CDA_anova)
print("CDA - 5 dots - 3 dots")
## [1] "CDA - 5 dots - 3 dots"
print(summary(CDA_5_3.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.0 0.4984 0.782 0.459
## Residuals 157 100.1 0.6376
## 8 observations deleted due to missingness
CDA_5_1.aov <- aov(CDA_5_1 ~ level, data = CDA_anova)
print("CDA - 5 dots - 1 dot")
## [1] "CDA - 5 dots - 1 dot"
print(summary(CDA_5_1.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.33 0.1675 0.3 0.741
## Residuals 157 87.73 0.5588
## 8 observations deleted due to missingness
CDA_3_1.aov <- aov(CDA_3_1 ~ level, data = CDA_anova)
print("CDA - 3 dots - 1 dot")
## [1] "CDA - 3 dots - 1 dot"
print(summary(CDA_3_1.aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.13 0.5650 0.958 0.386
## Residuals 157 92.59 0.5897
## 8 observations deleted due to missingness